Additional Figures
Accompanying ‘AE_TEMPLATE’
Sander W. van der Laan, PhD | s.w.vanderlaan@gmail.com
2023-06-14
General Setup
General Setup
Create a new analysis directories.
- general directory
- for plots
- for output of summary results
- for baseline tables
- for genetic analyses
- for Cox regression results
* General packages...
* Genomic packages...Background
This notebook contains additional figures of the project.
Loading data
Fix some variables
We need to get the ‘conventional unit’ versions of cholesterols.
Additional figures
Age and sex
We want to create per-age-group figures median ± interquartile range.
- Box and Whisker plot for PCSK9 plaque levels by sex.
- Box and Whisker plot for PCSK9 plaque levels by (sex and) age group (<55, 55-64, 65-74, 75-84, 85+).
ggpubr::ggboxplot(AERNASE.clin,
x = c("Gender"),
y = "PCSK9",
xlab = "gender",
ylab = "PCSK9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Gender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
library(dplyr)
AERNASE.clin <- AERNASE.clin %>% dplyr::mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
Age >= 55 & Age <= 64 ~ "55-64",
Age >= 65 & Age <= 74 ~ "65-74",
Age >= 75 & Age <= 84 ~ "75-84",
Age >= 85 ~ "85+")))
AERNASE.clin <- AERNASE.clin %>% dplyr::mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
Age >= 55 & Age <= 64 & Gender == "male"~ "55-64 males",
Age >= 65 & Age <= 74 & Gender == "male"~ "65-74 males",
Age >= 75 & Age <= 84 & Gender == "male"~ "75-84 males",
Age >= 85 & Gender == "male"~ "85+ males",
Age < 55 & Gender == "female" ~ "<55 females" ,
Age >= 55 & Age <= 64 & Gender == "female"~ "55-64 females ",
Age >= 65 & Age <= 74 & Gender == "female"~ "65-74 females",
Age >= 75 & Age <= 84 & Gender == "female"~ "75-84 females",
Age >= 85 & Gender == "female"~ "85+ females")))
table(AERNASE.clin$AgeGroup, AERNASE.clin$Gender)
female male
<55 11 27
55-64 43 124
65-74 58 189
75-84 38 119
85+ 4 9
<55 females <55 males 55-64 females 55-64 males 65-74 females 65-74 males 75-84 females 75-84 males 85+ females 85+ males
11 27 43 124 58 189 38 119 4 9 Now we can draw some graphs of plaque PCSK9 levels per sex and age group as median ± interquartile range.
# ?ggpubr::ggboxplot()
compare_means(PCSK9 ~ AgeGroup, data = AERNASE.clin, method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin,
x = c("AgeGroup"),
y = "PCSK9",
xlab = "Age groups (years)",
ylab = "PCSK9 (normalized expression)",
color = "AgeGroup",
palette = "npg",
# add = "median_iqr")
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AgeGroup.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
ggpubr::ggboxplot(AERNASE.clin,
x = c("AgeGroup"),
y = "PCSK9",
xlab = "Age groups (years) per gender",
ylab = "PCSK9 (normalized expression",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AgeGroup_perGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Hypertension & blood pressure
We want to create figures of PCSK9 levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs.
- Box and Whisker plot for PCSK9 plaque levels by hypertension group (no, yes)
- Box and Whisker plot for PCSK9 plaque levels by systolic blood pressure group (<120, 120-139, 140-159,160+)
library(dplyr)
AERNASE.clin <- AERNASE.clin %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
systolic >= 120 & systolic <= 139 ~ "120-139",
systolic >= 140 & systolic <= 159 ~ "140-159",
systolic >= 160 ~ "160+"))) mutate: new variable 'SBPGroup' (factor) with 5 unique values and 16% NA
female male
<120 7 22
120-139 30 81
140-159 36 120
160+ 62 167Now we can draw some graphs of plaque PCSK9 levels per sex and hypertension/blood pressure group as median ± interquartile range.
PCSK9
compare_means(PCSK9 ~ SBPGroup, data = AERNASE.clin %>% filter(!is.na(SBPGroup)), method = "kruskal.test")filter: removed 97 rows (16%), 525 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "PCSK9",
xlab = "Systolic blood pressure (mmHg)",
ylab = "PCSK9 (normalized expression)",
color = "SBPGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")filter: removed 97 rows (16%), 525 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.SBPGroup.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ Hypertension.selfreport, data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")filter: removed 13 rows (2%), 609 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "PCSK9",
xlab = "Self-reported hypertension",
ylab = "PCSK9 (normalized expression)",
color = "Hypertension.selfreport",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")filter: removed 13 rows (2%), 609 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ Hypertension.drugs, data = AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")filter: removed one row (<1%), 621 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "PCSK9",
xlab = "Hypertension medication use",
ylab = "PCSK9 (normalized expression)",
color = "Hypertension.drugs",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")filter: removed one row (<1%), 621 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.HypertensionDrugs.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ SBPGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(SBPGroup)), method = "kruskal.test")filter: removed 97 rows (16%), 525 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "PCSK9",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "PCSK9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")filter: removed 97 rows (16%), 525 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.SBPGroup_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ Hypertension.selfreport, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")filter: removed 13 rows (2%), 609 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "PCSK9",
xlab = "Self-reported hypertension per gender",
ylab = "PCSK9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")filter: removed 13 rows (2%), 609 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ Hypertension.drugs, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")filter: removed one row (<1%), 621 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "PCSK9",
xlab = "Hypertension medication use per gender",
ylab = "PCSK9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")filter: removed one row (<1%), 621 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ SBPGroup, group.by = "Hypertension.drugs", data = AERNASE.clin %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")filter: removed 98 rows (16%), 524 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)),
x = c("SBPGroup"),
y = "PCSK9",
xlab = "Systolic blood pressure (mmHg) by medication use",
ylab = "PCSK9 (normalized expression)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")filter: removed 98 rows (16%), 524 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")filter: removed 14 rows (2%), 608 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)),
x = c("Hypertension.selfreport"),
y = "PCSK9",
xlab = "Self-reported hypertension per medication use",
ylab = "PCSK9 (normalized expression)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")filter: removed 14 rows (2%), 608 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Hypercholesterolemia & LDL levels
We want to create figures of PCSK9 levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs.
- Box and Whisker plot for PCSK9 plaque levels by hypercholesterolemia
(
risk614) group (no, yes) - Box and Whisker plot for PCSK9 plaque levels by lipid-lowering drugs group (no, yes)
- Box and Whisker plot for PCSK9 plaque levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)
library(dplyr)
AERNASE.clin <- AERNASE.clin %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
LDL_finalCU >= 100 & LDL_finalCU <= 129 ~ "100-129",
LDL_finalCU >= 130 & LDL_finalCU <= 159 ~ "130-159",
LDL_finalCU >= 160 & LDL_finalCU <= 189 ~ "160-189",
LDL_finalCU >= 190 ~ "190+"))) mutate: new variable 'LDLGroup' (factor) with 6 unique values and 38% NA
female male
<100 45 141
100-129 25 73
130-159 18 42
160-189 9 20
190+ 2 9require(sjlabelled)
AERNASE.clin$risk614 <- to_factor(AERNASE.clin$risk614)
# Fix plaquephenotypes
attach(AERNASE.clin)
AERNASE.clin[,"Hypercholesterolemia"] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == "missing value"] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == -999] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == 0] <- "no"
AERNASE.clin$Hypercholesterolemia[risk614 == 1] <- "yes"
detach(AERNASE.clin)
table(AERNASE.clin$risk614, AERNASE.clin$Hypercholesterolemia)
no yes
0 190 0
1 0 404# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "risk614", "Hypercholesterolemia"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)Now we can draw some graphs of plaque PCSK9 levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users as median ± interquartile range.
PCSK9
compare_means(PCSK9 ~ LDLGroup, data = AERNASE.clin %>% filter(!is.na(LDLGroup)), method = "kruskal.test")filter: removed 238 rows (38%), 384 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "PCSK9",
xlab = "LDL (mg/dL) per gender",
ylab = "PCSK9 (normalized expression))",
color = "LDLGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")filter: removed 238 rows (38%), 384 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ LDLGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(LDLGroup)), method = "kruskal.test")filter: removed 238 rows (38%), 384 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "PCSK9",
xlab = "LDL (mg/dL) per gender",
ylab = "PCSK9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")filter: removed 238 rows (38%), 384 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ Hypercholesterolemia, data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")filter: removed 28 rows (5%), 594 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "PCSK9",
xlab = "Diagnosed hypercholesterolemia",
ylab = "PCSK9 (normalized expression))",
color = "Hypercholesterolemia",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")filter: removed 28 rows (5%), 594 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypercholesterolemia.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ Hypercholesterolemia, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")filter: removed 28 rows (5%), 594 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "PCSK9",
xlab = "Diagnosed hypercholesterolemia per gender",
ylab = "PCSK9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")filter: removed 28 rows (5%), 594 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ Med.Statin.LLD, data = AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")filter: removed one row (<1%), 621 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "PCSK9",
xlab = "Lipid-lowering drug use",
ylab = "PCSK9 (normalized expression))",
color = "Med.Statin.LLD",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")filter: removed one row (<1%), 621 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Med.Statin.LLD.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ Med.Statin.LLD, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")filter: removed one row (<1%), 621 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "PCSK9",
xlab = "Lipid-lowering drug use per gender",
ylab = "PCSK9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")filter: removed one row (<1%), 621 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AERNASE.clin %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")filter: removed 239 rows (38%), 383 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
x = c("LDLGroup"),
y = "PCSK9",
xlab = "LDL (mg/dL) per LLD use",
ylab = "PCSK9 (normalized expression))",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")filter: removed 239 rows (38%), 383 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")filter: removed 29 rows (5%), 593 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)),
x = c("Hypercholesterolemia"),
y = "PCSK9",
xlab = "Diagnosed hypercholesterolemia per LLD use",
ylab = "PCSK9 (normalized expression))",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")filter: removed 29 rows (5%), 593 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Kidney function (eGFR)
We want to create figures of PCSK9 levels stratified by kidney function.
- Box and Whisker plot for PCSK9 plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for PCSK9 plaque levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)
library(dplyr)
AERNASE.clin <- AERNASE.clin %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
GFR_MDRD >= 15 & GFR_MDRD <= 29 ~ "15-29",
GFR_MDRD >= 30 & GFR_MDRD <= 59 ~ "30-59",
GFR_MDRD >= 60 & GFR_MDRD <= 89 ~ "60-89",
GFR_MDRD >= 90 ~ "90+")))mutate: new variable 'eGFRGroup' (factor) with 5 unique values and 4% NA
female male
15-29 2 6
30-59 38 102
60-89 85 251
90+ 25 88
No data available/missing Normal kidney function CKD 2 (Mild) CKD 3 (Moderate) CKD 4 (Severe) CKD 5 (Failure)
15-29 0 0 0 0 8 0
30-59 0 0 0 140 0 0
60-89 0 0 336 0 0 0
90+ 0 113 0 0 0 0Now we can draw some graphs of plaque PCSK9 levels per sex and kidney function group as median ± interquartile range.
PCSK9
# Global test
compare_means(PCSK9 ~ eGFRGroup, data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")filter: removed 25 rows (4%), 597 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "PCSK9",
xlab = "eGFR (mL/min per 1.73 m2)",
ylab = "PCSK9 (normalized expression)",
color = "eGFRGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")filter: removed 25 rows (4%), 597 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.EGFR.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ eGFRGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")filter: removed 25 rows (4%), 597 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "PCSK9",
xlab = "eGFR (mL/min per 1.73 m2) per gender",
ylab = "PCSK9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")filter: removed 25 rows (4%), 597 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.EGFR_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ KDOQI, data = AERNASE.clin %>% filter(!is.na(KDOQI)), method = "kruskal.test")filter: removed 8 rows (1%), 614 rows remainingp1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "PCSK9",
xlab = "Kidney function (KDOQI)",
ylab = "PCSK9 (normalized expression)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")filter: removed 8 rows (1%), 614 rows remainingggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.KDOQI.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ KDOQI, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(KDOQI)), method = "kruskal.test")filter: removed 8 rows (1%), 614 rows remainingp1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "PCSK9",
xlab = "Kidney function (KDOQI) per gender",
ylab = "PCSK9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")filter: removed 8 rows (1%), 614 rows remainingggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.KDOQI_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ eGFRGroup, data = AERNASE.clin %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")filter: removed 25 rows (4%), 597 rows remainingp1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)),
x = c("eGFRGroup"),
y = "PCSK9",
xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
ylab = "PCSK9 (normalized expression)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")filter: removed 25 rows (4%), 597 rows remainingggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.EGFR_KDOQI.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
BMI
We want to create figures of PCSK9 levels stratified by BMI.
- Box and Whisker plot for PCSK9 plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for PCSK9 plaque levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)
library(dplyr)
AERNASE.clin <- AERNASE.clin %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
BMI >= 18.5 & BMI < 25 ~ "18.5-24",
BMI >= 25 & BMI < 30 ~ "25-29",
BMI >= 30 & BMI < 35 ~ "30-35",
BMI >= 35 ~ "35+"))) mutate: new variable 'BMIGroup' (factor) with 6 unique values and 5% NA# require(labelled)
# AERNASE.clin$BMI_US <- as_factor(AERNASE.clin$BMI_US)
# AERNASE.clin$BMI_WHO <- as_factor(AERNASE.clin$BMI_WHO)
# table(AERNASE.clin$BMI_WHO, AERNASE.clin$BMI_US)
table(AERNASE.clin$BMIGroup, AERNASE.clin$Gender)
female male
<18.5 3 2
18.5-24 46 161
25-29 69 220
30-35 18 54
35+ 6 12
No data available/missing Underweight Normal Overweight Obese
<18.5 0 5 0 0 0
18.5-24 0 0 207 0 0
25-29 0 0 0 288 0
30-35 0 0 0 0 72
35+ 0 0 0 0 18Now we can draw some graphs of plaque PCSK9 levels per sex and age group as median ± interquartile range.
PCSK9
# Global test
compare_means(PCSK9 ~ BMIGroup, data = AERNASE.clin %>% filter(!is.na(BMIGroup)), method = "kruskal.test")filter: removed 31 rows (5%), 591 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "PCSK9",
xlab = "BMI groups (kg/m2)",
ylab = "PCSK9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "BMIGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")filter: removed 31 rows (5%), 591 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.BMI.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ BMIGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(BMIGroup)), method = "kruskal.test")filter: removed 31 rows (5%), 591 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "PCSK9",
xlab = "BMI groups (kg/m2) per gender",
ylab = "PCSK9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")filter: removed 31 rows (5%), 591 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.BMI_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ BMIGroup, data = AERNASE.clin %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")filter: removed 32 rows (5%), 590 rows remainingp1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)),
x = c("BMIGroup"),
y = "PCSK9",
xlab = "BMI groups (kg/m2) per WHO categories",
ylab = "PCSK9 (normalized expression)",
color = "BMI_WHO",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")filter: removed 32 rows (5%), 590 rows remainingggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.BMI_byWHO.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Diabetes
We want to create figures of PCSK9 levels stratified by type 2 diabetes.
- Box and Whisker plot for PCSK9 plaque levels by type 2 diabetes group (no, yes)
Now we can draw some graphs of plaque PCSK9 levels per sex and age group as median ± interquartile range.
PCSK9
compare_means(PCSK9 ~ DiabetesStatus,
data = AERNASE.clin %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")filter: no rows removedggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "PCSK9",
xlab = "Diabetes status",
ylab = "PCSK9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "DiabetesStatus",
palette = "npg",
add = c("median_iqr", "jitter")) +
stat_compare_means(label = "p.format", method = "kruskal.test")filter: no rows removedggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Diabetes.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ DiabetesStatus, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")filter: no rows removedggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "PCSK9",
xlab = "Diabetes status per gender",
ylab = "PCSK9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")filter: no rows removedggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Diabetes_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Smoking
We want to create figures of PCSK9 levels stratified by smoking.
- Box and Whisker plot for PCSK9 plaque levels by smoking group (never, ex, current)
Now we can draw some graphs of plaque PCSK9 levels per sex and age group as median ± interquartile range.
PCSK9
# Global test
compare_means(PCSK9 ~ SmokerStatus, data = AERNASE.clin %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")filter: removed 23 rows (4%), 599 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "PCSK9",
xlab = "Smoker status",
ylab = "PCSK9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "SmokerStatus",
palette = "npg",
add = c("median_iqr", "jitter")) +
stat_compare_means(label = "p.format", method = "kruskal.test")filter: removed 23 rows (4%), 599 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Smoking.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ SmokerStatus, group.by ="Gender", data = AERNASE.clin %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")filter: removed 23 rows (4%), 599 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "PCSK9",
xlab = "Smoker status per gender",
ylab = "PCSK9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")filter: removed 23 rows (4%), 599 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Smoking_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Stenosis
We want to create figures of PCSK9 levels stratified by stenosis grade.
- Box and Whisker plot for PCSK9 plaque levels by stenosis grade group (<70, 70-89, 90+)
library(dplyr)
AERNASE.clin <- AERNASE.clin %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
stenose == "0-49%" ~ "<70",
stenose == "50-70%" ~ "<70",
stenose == "70-90%" ~ "70-89",
stenose == "50-99%" ~ "90+",
stenose == "70-99%" ~ "90+",
stenose == "100% (Occlusion)" ~ "90+",
stenose == "90-99%" ~ "90+")))mutate: new variable 'StenoticGroup' (factor) with 4 unique values and 4% NA
female male
<70 6 34
70-89 72 198
90+ 70 220
<70 70-89 90+
missing 0 0 0
0-49% 2 0 0
50-70% 38 0 0
70-90% 0 270 0
90-99% 0 0 284
100% (Occlusion) 0 0 5
NA 0 0 0
50-99% 0 0 1
70-99% 0 0 0
99 0 0 0Now we can draw some graphs of plaque PCSK9 levels per sex and age group as median ± interquartile range.
PCSK9
# Global test
compare_means(PCSK9 ~ StenoticGroup, data = AERNASE.clin %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")filter: removed 22 rows (4%), 600 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "PCSK9",
xlab = "Stenotic grade",
ylab = "PCSK9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "StenoticGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")filter: removed 22 rows (4%), 600 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Stenosis.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ StenoticGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")filter: removed 22 rows (4%), 600 rows remainingggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "PCSK9",
xlab = "Stenotic grade per gender",
ylab = "PCSK9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")filter: removed 22 rows (4%), 600 rows remainingggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Stenosis_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Symptoms
We want to create per-symptom figures.
Asymptomatic Symptomatic
<55 10 28
55-64 24 143
65-74 30 217
75-84 15 141
85+ 1 12
Asymptomatic Symptomatic
female 15 138
male 65 403
Asymptomatic Symptomatic
80 541 Now we can draw some graphs of plaque PCSK9 levels per symptom group as median ± interquartile range.
PCSK9
# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))
p1 <- ggpubr::ggboxplot(AERNASE.clin,
x = "AsymptSympt2G", y = "PCSK9",
title = "PCSK9 (normalized expression) levels per symptom",
xlab = "Symptoms",
ylab = "PCSK9 (normalized expression)",
color = "AsymptSympt2G",
# palette = c(uithof_color[16], uithof_color[23]),
palette = "npg",
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AsymptSympt2G.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
rm(p1)
compare_means(PCSK9 ~ AsymptSympt2G, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin,
x = "AsymptSympt2G", y = "PCSK9",
title = "PCSK9 (normalized expression) levels per symptom by gender",
xlab = "Symptoms",
ylab = "PCSK9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AsymptSympt2G.byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Forest plots
We would also like to visualize the multivariable analyses results.
library(ggplot2)
library(openxlsx)
model1_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.targets.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"))
model2_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.targets.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"))
model1_target$model <- "univariate"
model2_target$model <- "multivariate"
models_target <- rbind(model1_target, model2_target)
models_targetForest plots.
PCSK9
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"),
levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
cen = c(models_target$OR[models_target$Predictor=="PCSK9"]),
low = c(models_target$low95CI[models_target$Predictor=="PCSK9"]),
high = c(models_target$up95CI[models_target$Predictor=="PCSK9"]))
fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) +
geom_hline(yintercept = 1, lty = 2) + # add a dotted line at x=1 after flip
coord_flip(ylim = c(0.8, 1.7)) + # flip coordinates (puts labels on y axis)
xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
ggtitle("Plaque PCSK9 normalized expression (1 SD increment, n = 622)") +
theme_minimal() # use a white background
print(fp)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.forest.pdf"), plot = fp)Saving 7.29 x 4.51 in image
Target expression vs. cytokines plaque levels correlations
We will plot the correlations of other cytokine plaque levels to the PCSK9 plaque levels. These include:
- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB
In addition we will look at three metalloproteinases which were measured using an activity assay.
- MMP2
- MMP8
- MMP9
The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the PCSK9` plaque levels.
We will set the measurements that yielded ‘0’ to NA, as it is unlikely that any protein ever has exactly 0 copies. The ‘0’ yielded during the experiment are due to the limits of the detection.
Prepare data
# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"
# fix names
names(AERNASE.clin)[names(AERNASE.clin) == "IL6"] <- "IL6rna"
cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21",
"INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1",
"TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")
AERNASE.clin <- merge(AERNASE.clin,
subset(AEDB.CEA, select = c("STUDY_NUMBER",
cytokines,
metalloproteinases)),
by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)
proteins_of_interest <- c(cytokines, metalloproteinases)
proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))
# make variables numerics()
AERNASE.clin <- AERNASE.clin %>%
mutate_each(funs(as.numeric), proteins_of_interest)Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))Warning: `mutate_each_()` was deprecated in dplyr 0.7.0.
Please use `across()` instead.Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
Please use `all_of()` or `any_of()` instead.
# Was:
data %>% select(proteins_of_interest)
# Now:
data %>% select(all_of(proteins_of_interest))
See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
for(PROTEIN in 1:length(proteins_of_interest)){
var.temp.rank = proteins_of_interest_rank[PROTEIN]
var.temp = proteins_of_interest[PROTEIN]
cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
cat(paste0("* changing ", var.temp, " to numeric.\n"))
# AERNASE.clin <- AERNASE.clin %>% mutate(AERNASE.clin[,var.temp] == replace(AERNASE.clin[,var.temp], AERNASE.clin[,var.temp]==0, NA))
AERNASE.clin[,var.temp][AERNASE.clin[,var.temp]==0.000000]=NA
cat(paste0("* standardising ", var.temp,
" (mean: ",round(mean(!is.na(AERNASE.clin[,var.temp])), digits = 6),
", n = ",sum(!is.na(AERNASE.clin[,var.temp])),").\n"))
AERNASE.clin <- AERNASE.clin %>%
mutate_at(vars(var.temp),
# list(Z = ~ (AERNASE.clin[,var.temp] - mean(AERNASE.clin[,var.temp], na.rm = TRUE))/sd(AERNASE.clin[,var.temp], na.rm = TRUE))
list(RANK = ~ qnorm((rank(AERNASE.clin[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin[,var.temp]))))
)
# str(UCORBIOGSAqc$Z)
cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
AERNASE.clin[,var.temp.rank] <- NULL
names(AERNASE.clin)[names(AERNASE.clin) == "RANK"] <- var.temp.rank
}
Selecting IL2 and standardising: IL2_rank.
* changing IL2 to numeric.
* standardising IL2 (mean: 0.292605, n = 182).Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
Please use `all_of()` or `any_of()` instead.
# Was:
data %>% select(var.temp)
# Now:
data %>% select(all_of(var.temp))
See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.mutate_at: new variable 'RANK' (double) with 183 unique values and 71% NA* renaming RANK to IL2_rank.
Selecting IL4 and standardising: IL4_rank.
* changing IL4 to numeric.
* standardising IL4 (mean: 0.263666, n = 164).mutate_at: new variable 'RANK' (double) with 165 unique values and 74% NA* renaming RANK to IL4_rank.
Selecting IL5 and standardising: IL5_rank.
* changing IL5 to numeric.
* standardising IL5 (mean: 0.287781, n = 179).mutate_at: new variable 'RANK' (double) with 180 unique values and 71% NA* renaming RANK to IL5_rank.
Selecting IL6 and standardising: IL6_rank.
* changing IL6 to numeric.
* standardising IL6 (mean: 0.303859, n = 189).mutate_at: new variable 'RANK' (double) with 190 unique values and 70% NA* renaming RANK to IL6_rank.
Selecting IL8 and standardising: IL8_rank.
* changing IL8 to numeric.
* standardising IL8 (mean: 0.289389, n = 180).mutate_at: new variable 'RANK' (double) with 181 unique values and 71% NA* renaming RANK to IL8_rank.
Selecting IL9 and standardising: IL9_rank.
* changing IL9 to numeric.
* standardising IL9 (mean: 0.337621, n = 210).mutate_at: new variable 'RANK' (double) with 211 unique values and 66% NA* renaming RANK to IL9_rank.
Selecting IL10 and standardising: IL10_rank.
* changing IL10 to numeric.
* standardising IL10 (mean: 0.252412, n = 157).mutate_at: new variable 'RANK' (double) with 158 unique values and 75% NA* renaming RANK to IL10_rank.
Selecting IL12 and standardising: IL12_rank.
* changing IL12 to numeric.
* standardising IL12 (mean: 0.266881, n = 166).mutate_at: new variable 'RANK' (double) with 167 unique values and 73% NA* renaming RANK to IL12_rank.
Selecting IL13 and standardising: IL13_rank.
* changing IL13 to numeric.
* standardising IL13 (mean: 0.369775, n = 230).mutate_at: new variable 'RANK' (double) with 231 unique values and 63% NA* renaming RANK to IL13_rank.
Selecting IL21 and standardising: IL21_rank.
* changing IL21 to numeric.
* standardising IL21 (mean: 0.369775, n = 230).mutate_at: new variable 'RANK' (double) with 231 unique values and 63% NA* renaming RANK to IL21_rank.
Selecting INFG and standardising: INFG_rank.
* changing INFG to numeric.
* standardising INFG (mean: 0.278135, n = 173).mutate_at: new variable 'RANK' (double) with 174 unique values and 72% NA* renaming RANK to INFG_rank.
Selecting TNFA and standardising: TNFA_rank.
* changing TNFA to numeric.
* standardising TNFA (mean: 0.263666, n = 164).mutate_at: new variable 'RANK' (double) with 165 unique values and 74% NA* renaming RANK to TNFA_rank.
Selecting MIF and standardising: MIF_rank.
* changing MIF to numeric.
* standardising MIF (mean: 0.369775, n = 230).mutate_at: new variable 'RANK' (double) with 231 unique values and 63% NA* renaming RANK to MIF_rank.
Selecting MCP1 and standardising: MCP1_rank.
* changing MCP1 to numeric.
* standardising MCP1 (mean: 0.366559, n = 228).mutate_at: new variable 'RANK' (double) with 229 unique values and 63% NA* renaming RANK to MCP1_rank.
Selecting MIP1a and standardising: MIP1a_rank.
* changing MIP1a to numeric.
* standardising MIP1a (mean: 0.344051, n = 214).mutate_at: new variable 'RANK' (double) with 215 unique values and 66% NA* renaming RANK to MIP1a_rank.
Selecting RANTES and standardising: RANTES_rank.
* changing RANTES to numeric.
* standardising RANTES (mean: 0.363344, n = 226).mutate_at: new variable 'RANK' (double) with 227 unique values and 64% NA* renaming RANK to RANTES_rank.
Selecting MIG and standardising: MIG_rank.
* changing MIG to numeric.
* standardising MIG (mean: 0.364952, n = 227).mutate_at: new variable 'RANK' (double) with 228 unique values and 64% NA* renaming RANK to MIG_rank.
Selecting IP10 and standardising: IP10_rank.
* changing IP10 to numeric.
* standardising IP10 (mean: 0.332797, n = 207).mutate_at: new variable 'RANK' (double) with 208 unique values and 67% NA* renaming RANK to IP10_rank.
Selecting Eotaxin1 and standardising: Eotaxin1_rank.
* changing Eotaxin1 to numeric.
* standardising Eotaxin1 (mean: 0.369775, n = 230).mutate_at: new variable 'RANK' (double) with 231 unique values and 63% NA* renaming RANK to Eotaxin1_rank.
Selecting TARC and standardising: TARC_rank.
* changing TARC to numeric.
* standardising TARC (mean: 0.326367, n = 203).mutate_at: new variable 'RANK' (double) with 204 unique values and 67% NA* renaming RANK to TARC_rank.
Selecting PARC and standardising: PARC_rank.
* changing PARC to numeric.
* standardising PARC (mean: 0.369775, n = 230).mutate_at: new variable 'RANK' (double) with 231 unique values and 63% NA* renaming RANK to PARC_rank.
Selecting MDC and standardising: MDC_rank.
* changing MDC to numeric.
* standardising MDC (mean: 0.345659, n = 215).mutate_at: new variable 'RANK' (double) with 216 unique values and 65% NA* renaming RANK to MDC_rank.
Selecting OPG and standardising: OPG_rank.
* changing OPG to numeric.
* standardising OPG (mean: 0.369775, n = 230).mutate_at: new variable 'RANK' (double) with 231 unique values and 63% NA* renaming RANK to OPG_rank.
Selecting sICAM1 and standardising: sICAM1_rank.
* changing sICAM1 to numeric.
* standardising sICAM1 (mean: 0.369775, n = 230).mutate_at: new variable 'RANK' (double) with 231 unique values and 63% NA* renaming RANK to sICAM1_rank.
Selecting VEGFA and standardising: VEGFA_rank.
* changing VEGFA to numeric.
* standardising VEGFA (mean: 0.323151, n = 201).mutate_at: new variable 'RANK' (double) with 202 unique values and 68% NA* renaming RANK to VEGFA_rank.
Selecting TGFB and standardising: TGFB_rank.
* changing TGFB to numeric.
* standardising TGFB (mean: 0.371383, n = 231).mutate_at: new variable 'RANK' (double) with 232 unique values and 63% NA* renaming RANK to TGFB_rank.
Selecting MMP2 and standardising: MMP2_rank.
* changing MMP2 to numeric.
* standardising MMP2 (mean: 0.371383, n = 231).mutate_at: new variable 'RANK' (double) with 232 unique values and 63% NA* renaming RANK to MMP2_rank.
Selecting MMP8 and standardising: MMP8_rank.
* changing MMP8 to numeric.
* standardising MMP8 (mean: 0.371383, n = 231).mutate_at: new variable 'RANK' (double) with 232 unique values and 63% NA* renaming RANK to MMP8_rank.
Selecting MMP9 and standardising: MMP9_rank.
* changing MMP9 to numeric.
* standardising MMP9 (mean: 0.371383, n = 231).mutate_at: new variable 'RANK' (double) with 232 unique values and 63% NA* renaming RANK to MMP9_rank.Visualize transformations
We will just visualize these transformations.
proteins_of_interest_rank_target <- c("PCSK9", proteins_of_interest_rank)
proteins_of_interest_target <- c("PCSK9", proteins_of_interest)
for(PROTEIN_GENE in proteins_of_interest_target){
cat(paste0("Plotting protein ", PROTEIN_GENE, ".\n"))
p1 <- ggpubr::gghistogram(AERNASE.clin, PROTEIN_GENE,
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = paste0(PROTEIN_GENE, " (normalized expression)"),
xlab = "",
ggtheme = theme_minimal())
print(p1)
}Plotting protein PCSK9.Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.Plotting protein IL2.
Plotting protein IL4.
Plotting protein IL5.
Plotting protein IL6.
Plotting protein IL8.
Plotting protein IL9.
Plotting protein IL10.
Plotting protein IL12.
Plotting protein IL13.
Plotting protein IL21.
Plotting protein INFG.
Plotting protein TNFA.
Plotting protein MIF.
Plotting protein MCP1.
Plotting protein MIP1a.
Plotting protein RANTES.
Plotting protein MIG.
Plotting protein IP10.
Plotting protein Eotaxin1.
Plotting protein TARC.
Plotting protein PARC.
Plotting protein MDC.
Plotting protein OPG.
Plotting protein sICAM1.
Plotting protein VEGFA.
Plotting protein TGFB.
Plotting protein MMP2.
Plotting protein MMP8.
Plotting protein MMP9.
for(PROTEIN_GENE in proteins_of_interest_rank_target){
cat(paste0("Plotting protein ", PROTEIN_GENE, ".\n"))
p1 <- ggpubr::gghistogram(AERNASE.clin, PROTEIN_GENE,
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = paste0(PROTEIN_GENE, " (normalized expression)"),
xlab = "inverse-normal transformation",
ggtheme = theme_minimal())
print(p1)
}Plotting protein PCSK9.Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.Plotting protein IL2_rank.
Plotting protein IL4_rank.
Plotting protein IL5_rank.
Plotting protein IL6_rank.
Plotting protein IL8_rank.
Plotting protein IL9_rank.
Plotting protein IL10_rank.
Plotting protein IL12_rank.
Plotting protein IL13_rank.
Plotting protein IL21_rank.
Plotting protein INFG_rank.
Plotting protein TNFA_rank.
Plotting protein MIF_rank.
Plotting protein MCP1_rank.
Plotting protein MIP1a_rank.
Plotting protein RANTES_rank.
Plotting protein MIG_rank.
Plotting protein IP10_rank.
Plotting protein Eotaxin1_rank.
Plotting protein TARC_rank.
Plotting protein PARC_rank.
Plotting protein MDC_rank.
Plotting protein OPG_rank.
Plotting protein sICAM1_rank.
Plotting protein VEGFA_rank.
Plotting protein TGFB_rank.
Plotting protein MMP2_rank.
Plotting protein MMP8_rank.
Plotting protein MMP9_rank.
Correlations
Here we calculate correlations between PCSK9 and 28 other cytokines. We use Spearman’s test, thus, correlations a given in rho. Please note the indications of measurement methods:
- L: LUMINEX
- E: ELISA
- a: activity assay
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages.auto("devtools")
devtools::install_github("kassambara/ggcorrplot")Using github PAT from envvar GITHUB_PAT
Skipping install of 'ggcorrplot' from a github remote, the SHA1 (0a85456d) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
temp <- subset(AERNASE.clin,
select = c(proteins_of_interest_rank_target)
)
# str(AEDB.CEA.temp)
matrix.RANK <- as.matrix(temp)
rm(temp)
corr_biomarkers.rank <- round(cor(matrix.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
rename_proteins_of_interest_target <- c("PCSK9 (RNA)",
"IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12",
"IL13 (L)", "IL21 (L)",
"INFG", "TNFA", "MIF (L)",
"MCP1 (L)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)",
"Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)",
"OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(matrix.RANK, use = "pairwise.complete.obs", method = "spearman")Warning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with ties# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
row = rownames(cormat)[row(cormat)[ut]],
column = rownames(cormat)[col(cormat)[ut]],
cor =(cormat)[ut],
p = pmat[ut]
)
}
corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"
DT::datatable(corr_biomarkers.rank.df)# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
tl.cex = 16,
# xlab = c("MCP1"),
# p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())Saving 7.29 x 4.51 in imageSaving 7.29 x 4.51 in image
While visually attractive we are not necessarily interested in the correlations between all the cytokines, rather of PCSK9` with other cytokines only.
PCSK9
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "PCSK9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold[1] 2.763428p1 <- ggpubr::ggbarplot(temp,
x = "CytokineY",
y = "SpearmanRho",
fill = "CytokineY", # change fill color by cyl
# color = "white", # Set bar border colors to white
# palette = uithof_color, # jco journal color palett. see ?ggpar
xlab = "Cytokine",
sort.val = "desc", # Sort the value in dscending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 45, # Rotate vertically x axis texts
cex = 1.25
)
ggpar(p1, legend = "bottom",
legend.title = "") +
theme(axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18)) +
labs(y = expression(paste("Spearman's"~italic(rho))))
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.png"), plot = last_plot())Saving 7.29 x 4.51 in imageggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Another version - probably not good.
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "PCSK9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold[1] 2.763428p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
color = "CytokineY", #fill = "CytokineY", # Color by groups
# palette = uithof_color, # Custom color palette
xlab = "Cytokine",
# ylab = expression(log[10]~"("~italic(p)~")-value"),
# ylim = c(0, 9),
sorting = "descending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
rotate = FALSE, # Rotate vertically
# group = "CytokineY", # Order by groups
dot.size = 8, # Large dot size
label = round(temp$SpearmanRho, digits = 3), # Add mpg values as dot labels
font.label = list(color = "white", size = 4,
vjust = 0.5)
)
ggpar(p1, legend = "",
legend.title = "") +
theme(axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18)) +
labs(y = expression(log[10]~"("~italic(p)~")-value"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.dotchart.png"), plot = last_plot())Saving 7.29 x 4.51 in imageggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.dotchart.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Target expression vs. cytokines plaque levels lm()
Model 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of plaque cytokines traits as a function of plaque PCSK9 levels.
Running linear regression...for (protein in 1:length(TRAITS.TARGET.RANK)) {
PROTEIN = TRAITS.TARGET.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(proteins_of_interest_rank)) {
TRAIT = proteins_of_interest_rank[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AERNASE.clin %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
# fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of CXCL10.
- processing IL2_rankWarning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
Please use `all_of()` or `any_of()` instead.
# Was:
data %>% select(PROTEIN)
# Now:
data %>% select(all_of(PROTEIN))
See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.filter: removed 440 rows (71%), 182 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-20.593910 0.001813
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2587 -1.9577 -1.0634 0.0277 30.4990
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.891e+01 1.185e+01 -1.596 0.1124
currentDF[, TRAIT] 1.127e-01 3.289e-01 0.343 0.7322
Age -2.803e-02 3.970e-02 -0.706 0.4811
Gendermale 7.476e-01 7.702e-01 0.971 0.3330
ORdate_epoch 1.784e-03 9.375e-04 1.903 0.0586 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.346 on 177 degrees of freedom
Multiple R-squared: 0.02891, Adjusted R-squared: 0.006963
F-statistic: 1.317 on 4 and 177 DF, p-value: 0.2653
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL2_rank
Effect size...............: 0.112729
Standard error............: 0.328888
Odds ratio (effect size)..: 1.119
Lower 95% CI..............: 0.587
Upper 95% CI..............: 2.133
T-value...................: 0.342756
P-value...................: 0.7321886
R^2.......................: 0.028908
Adjusted r^2..............: 0.006963
Sample size of AE DB......: 622
Sample size of model......: 182
Missing data %............: 70.73955
- processing IL4_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-27.527084 0.002387
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5858 -2.1639 -1.0114 0.0978 30.2270
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -25.195774 13.932271 -1.808 0.0724 .
currentDF[, TRAIT] 0.013329 0.377219 0.035 0.9719
Age -0.025919 0.045149 -0.574 0.5667
Gendermale 0.928555 0.885623 1.048 0.2960
ORdate_epoch 0.002283 0.001106 2.065 0.0406 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.675 on 159 degrees of freedom
Multiple R-squared: 0.03722, Adjusted R-squared: 0.01299
F-statistic: 1.536 on 4 and 159 DF, p-value: 0.1941
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL4_rank
Effect size...............: 0.013329
Standard error............: 0.377219
Odds ratio (effect size)..: 1.013
Lower 95% CI..............: 0.484
Upper 95% CI..............: 2.123
T-value...................: 0.035334
P-value...................: 0.971858
R^2.......................: 0.037215
Adjusted r^2..............: 0.012994
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing IL5_rankfilter: removed 443 rows (71%), 179 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-24.929820 0.002172
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5362 -2.1010 -1.0818 0.0518 30.2894
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -23.113669 12.789076 -1.807 0.0724 .
currentDF[, TRAIT] 0.066669 0.348968 0.191 0.8487
Age -0.026370 0.042169 -0.625 0.5326
Gendermale 0.987344 0.819466 1.205 0.2299
ORdate_epoch 0.002108 0.001025 2.057 0.0412 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.553 on 174 degrees of freedom
Multiple R-squared: 0.03529, Adjusted R-squared: 0.01312
F-statistic: 1.591 on 4 and 174 DF, p-value: 0.1787
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL5_rank
Effect size...............: 0.066669
Standard error............: 0.348968
Odds ratio (effect size)..: 1.069
Lower 95% CI..............: 0.539
Upper 95% CI..............: 2.118
T-value...................: 0.191045
P-value...................: 0.8487128
R^2.......................: 0.035292
Adjusted r^2..............: 0.013115
Sample size of AE DB......: 622
Sample size of model......: 179
Missing data %............: 71.22186
- processing IL6_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-17.243264 0.001542
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2515 -1.9304 -1.1086 -0.0916 30.5808
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.644e+01 1.116e+01 -1.473 0.1425
currentDF[, TRAIT] 9.355e-03 3.329e-01 0.028 0.9776
Age -1.641e-02 4.031e-02 -0.407 0.6844
Gendermale 8.476e-01 7.512e-01 1.128 0.2607
ORdate_epoch 1.516e-03 8.908e-04 1.702 0.0905 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.452 on 184 degrees of freedom
Multiple R-squared: 0.02396, Adjusted R-squared: 0.002742
F-statistic: 1.129 on 4 and 184 DF, p-value: 0.3441
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL6_rank
Effect size...............: 0.009355
Standard error............: 0.332899
Odds ratio (effect size)..: 1.009
Lower 95% CI..............: 0.526
Upper 95% CI..............: 1.938
T-value...................: 0.0281
P-value...................: 0.9776125
R^2.......................: 0.02396
Adjusted r^2..............: 0.002742
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing IL8_rankfilter: removed 442 rows (71%), 180 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-15.594343 0.001404
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.166 -1.843 -1.106 0.005 30.667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.299e+01 1.130e+01 -1.150 0.252
currentDF[, TRAIT] 1.272e-01 3.372e-01 0.377 0.706
Age -3.365e-02 3.858e-02 -0.872 0.384
Gendermale 4.626e-01 7.677e-01 0.603 0.548
ORdate_epoch 1.349e-03 8.979e-04 1.502 0.135
Residual standard error: 4.323 on 175 degrees of freedom
Multiple R-squared: 0.0221, Adjusted R-squared: -0.0002564
F-statistic: 0.9885 on 4 and 175 DF, p-value: 0.4152
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL8_rank
Effect size...............: 0.127242
Standard error............: 0.337158
Odds ratio (effect size)..: 1.136
Lower 95% CI..............: 0.586
Upper 95% CI..............: 2.199
T-value...................: 0.377395
P-value...................: 0.7063371
R^2.......................: 0.022096
Adjusted r^2..............: -0.000256
Sample size of AE DB......: 622
Sample size of model......: 180
Missing data %............: 71.06109
- processing IL9_rankfilter: removed 412 rows (66%), 210 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-14.168996 0.001293
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.1193 -1.9856 -1.2093 0.1884 30.4120
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.331e+01 9.930e+00 -1.341 0.1815
currentDF[, TRAIT] 3.405e-01 3.054e-01 1.115 0.2662
Age -3.870e-02 3.768e-02 -1.027 0.3056
Gendermale 6.756e-01 7.021e-01 0.962 0.3370
ORdate_epoch 1.392e-03 7.602e-04 1.831 0.0685 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.382 on 205 degrees of freedom
Multiple R-squared: 0.03046, Adjusted R-squared: 0.01154
F-statistic: 1.61 on 4 and 205 DF, p-value: 0.1731
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL9_rank
Effect size...............: 0.340451
Standard error............: 0.305389
Odds ratio (effect size)..: 1.406
Lower 95% CI..............: 0.773
Upper 95% CI..............: 2.557
T-value...................: 1.114812
P-value...................: 0.2662361
R^2.......................: 0.030458
Adjusted r^2..............: 0.01154
Sample size of AE DB......: 622
Sample size of model......: 210
Missing data %............: 66.23794
- processing IL10_rankfilter: removed 465 rows (75%), 157 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-22.793267 0.001995
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.8876 -1.9775 -1.1854 0.0065 30.1392
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -23.377853 14.266930 -1.639 0.1034
currentDF[, TRAIT] 0.411129 0.375707 1.094 0.2756
Age -0.019509 0.044705 -0.436 0.6632
Gendermale 0.880991 0.866269 1.017 0.3108
ORdate_epoch 0.002095 0.001120 1.871 0.0633 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.514 on 152 degrees of freedom
Multiple R-squared: 0.03512, Adjusted R-squared: 0.009728
F-statistic: 1.383 on 4 and 152 DF, p-value: 0.2424
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL10_rank
Effect size...............: 0.411129
Standard error............: 0.375707
Odds ratio (effect size)..: 1.509
Lower 95% CI..............: 0.722
Upper 95% CI..............: 3.15
T-value...................: 1.094281
P-value...................: 0.2755628
R^2.......................: 0.03512
Adjusted r^2..............: 0.009728
Sample size of AE DB......: 622
Sample size of model......: 157
Missing data %............: 74.75884
- processing IL12_rankfilter: removed 456 rows (73%), 166 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-25.814559 0.002247
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.6741 -2.1470 -1.0721 0.0146 30.1903
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -23.656542 13.933331 -1.698 0.0915 .
currentDF[, TRAIT] 0.103256 0.372317 0.277 0.7819
Age -0.026438 0.045110 -0.586 0.5587
Gendermale 0.925473 0.868952 1.065 0.2884
ORdate_epoch 0.002161 0.001103 1.959 0.0518 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.66 on 161 degrees of freedom
Multiple R-squared: 0.03474, Adjusted R-squared: 0.01076
F-statistic: 1.449 on 4 and 161 DF, p-value: 0.2204
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL12_rank
Effect size...............: 0.103256
Standard error............: 0.372317
Odds ratio (effect size)..: 1.109
Lower 95% CI..............: 0.534
Upper 95% CI..............: 2.3
T-value...................: 0.277334
P-value...................: 0.7818795
R^2.......................: 0.034741
Adjusted r^2..............: 0.01076
Sample size of AE DB......: 622
Sample size of model......: 166
Missing data %............: 73.3119
- processing IL13_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-14.519110 0.001321
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.0146 -2.0196 -1.2092 0.1909 30.4644
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.304e+01 9.733e+00 -1.340 0.1816
currentDF[, TRAIT] 3.245e-01 2.924e-01 1.110 0.2683
Age -2.700e-02 3.562e-02 -0.758 0.4492
Gendermale 6.793e-01 6.698e-01 1.014 0.3116
ORdate_epoch 1.309e-03 7.513e-04 1.742 0.0828 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.369 on 225 degrees of freedom
Multiple R-squared: 0.02751, Adjusted R-squared: 0.01022
F-statistic: 1.591 on 4 and 225 DF, p-value: 0.1775
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL13_rank
Effect size...............: 0.324491
Standard error............: 0.292429
Odds ratio (effect size)..: 1.383
Lower 95% CI..............: 0.78
Upper 95% CI..............: 2.454
T-value...................: 1.109641
P-value...................: 0.2683382
R^2.......................: 0.027513
Adjusted r^2..............: 0.010224
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing IL21_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-14.519110 0.001321
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.6627 -2.0106 -1.2234 0.0735 30.5088
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.342e+01 9.745e+00 -1.377 0.1698
currentDF[, TRAIT] 1.698e-01 2.920e-01 0.582 0.5615
Age -2.977e-02 3.559e-02 -0.836 0.4038
Gendermale 6.966e-01 6.719e-01 1.037 0.3009
ORdate_epoch 1.353e-03 7.515e-04 1.800 0.0732 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.378 on 225 degrees of freedom
Multiple R-squared: 0.02366, Adjusted R-squared: 0.006301
F-statistic: 1.363 on 4 and 225 DF, p-value: 0.2477
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL21_rank
Effect size...............: 0.16982
Standard error............: 0.292023
Odds ratio (effect size)..: 1.185
Lower 95% CI..............: 0.669
Upper 95% CI..............: 2.101
T-value...................: 0.58153
P-value...................: 0.5614655
R^2.......................: 0.023658
Adjusted r^2..............: 0.006301
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing INFG_rankfilter: removed 449 rows (72%), 173 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-22.830645 0.002005
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.2760 -2.0839 -1.1621 0.0481 29.9023
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -26.341077 13.025803 -2.022 0.0447 *
currentDF[, TRAIT] 0.477658 0.375693 1.271 0.2053
Age -0.018453 0.043075 -0.428 0.6689
Gendermale 1.063952 0.858060 1.240 0.2167
ORdate_epoch 0.002321 0.001025 2.265 0.0248 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.557 on 168 degrees of freedom
Multiple R-squared: 0.04099, Adjusted R-squared: 0.01816
F-statistic: 1.795 on 4 and 168 DF, p-value: 0.1321
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: INFG_rank
Effect size...............: 0.477658
Standard error............: 0.375693
Odds ratio (effect size)..: 1.612
Lower 95% CI..............: 0.772
Upper 95% CI..............: 3.367
T-value...................: 1.271405
P-value...................: 0.2053419
R^2.......................: 0.040992
Adjusted r^2..............: 0.018158
Sample size of AE DB......: 622
Sample size of model......: 173
Missing data %............: 72.18649
- processing TNFA_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-21.953259 0.001931
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5656 -2.0484 -1.2071 -0.1664 30.2991
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -21.026177 13.900491 -1.513 0.1324
currentDF[, TRAIT] 0.161735 0.371858 0.435 0.6642
Age -0.011825 0.045472 -0.260 0.7952
Gendermale 1.070173 0.863104 1.240 0.2168
ORdate_epoch 0.001856 0.001096 1.693 0.0924 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.686 on 159 degrees of freedom
Multiple R-squared: 0.02936, Adjusted R-squared: 0.004943
F-statistic: 1.202 on 4 and 159 DF, p-value: 0.3119
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: TNFA_rank
Effect size...............: 0.161735
Standard error............: 0.371858
Odds ratio (effect size)..: 1.176
Lower 95% CI..............: 0.567
Upper 95% CI..............: 2.437
T-value...................: 0.434937
P-value...................: 0.664198
R^2.......................: 0.029362
Adjusted r^2..............: 0.004943
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing MIF_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-14.519110 0.001321
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4140 -1.9811 -1.2029 -0.0171 30.6314
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.359e+01 1.091e+01 -1.246 0.214
currentDF[, TRAIT] -3.284e-03 3.251e-01 -0.010 0.992
Age -3.167e-02 3.551e-02 -0.892 0.373
Gendermale 7.277e-01 6.703e-01 1.085 0.279
ORdate_epoch 1.375e-03 8.416e-04 1.633 0.104
Residual standard error: 4.381 on 225 degrees of freedom
Multiple R-squared: 0.02219, Adjusted R-squared: 0.004808
F-statistic: 1.277 on 4 and 225 DF, p-value: 0.28
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MIF_rank
Effect size...............: -0.003284
Standard error............: 0.325134
Odds ratio (effect size)..: 0.997
Lower 95% CI..............: 0.527
Upper 95% CI..............: 1.885
T-value...................: -0.0101
P-value...................: 0.9919505
R^2.......................: 0.022191
Adjusted r^2..............: 0.004808
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing MCP1_rankfilter: removed 394 rows (63%), 228 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-14.202277 0.001297
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3841 -2.0118 -1.2259 -0.0199 30.6167
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.316e+01 1.002e+01 -1.313 0.1905
currentDF[, TRAIT] -4.605e-02 3.000e-01 -0.153 0.8781
Age -3.057e-02 3.606e-02 -0.848 0.3974
Gendermale 7.207e-01 6.791e-01 1.061 0.2897
ORdate_epoch 1.336e-03 7.702e-04 1.735 0.0842 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.399 on 223 degrees of freedom
Multiple R-squared: 0.02118, Adjusted R-squared: 0.003627
F-statistic: 1.207 on 4 and 223 DF, p-value: 0.3089
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MCP1_rank
Effect size...............: -0.046054
Standard error............: 0.300037
Odds ratio (effect size)..: 0.955
Lower 95% CI..............: 0.53
Upper 95% CI..............: 1.719
T-value...................: -0.153494
P-value...................: 0.8781474
R^2.......................: 0.021184
Adjusted r^2..............: 0.003627
Sample size of AE DB......: 622
Sample size of model......: 228
Missing data %............: 63.34405
- processing MIP1a_rankfilter: removed 408 rows (66%), 214 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-13.44928 0.00124
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.6098 -2.0117 -1.2591 -0.0956 30.4804
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.255e+01 1.017e+01 -1.235 0.218
currentDF[, TRAIT] 1.249e-01 3.105e-01 0.402 0.688
Age -3.174e-02 3.777e-02 -0.840 0.402
Gendermale 7.489e-01 7.227e-01 1.036 0.301
ORdate_epoch 1.294e-03 7.811e-04 1.657 0.099 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.516 on 209 degrees of freedom
Multiple R-squared: 0.02134, Adjusted R-squared: 0.002605
F-statistic: 1.139 on 4 and 209 DF, p-value: 0.3391
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.124914
Standard error............: 0.310533
Odds ratio (effect size)..: 1.133
Lower 95% CI..............: 0.616
Upper 95% CI..............: 2.082
T-value...................: 0.402255
P-value...................: 0.6879072
R^2.......................: 0.021335
Adjusted r^2..............: 0.002605
Sample size of AE DB......: 622
Sample size of model......: 214
Missing data %............: 65.59485
- processing RANTES_rankfilter: removed 396 rows (64%), 226 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-14.167115 0.001295
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5560 -2.0074 -1.2026 0.0459 30.6277
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.700e+01 1.095e+01 -1.552 0.122
currentDF[, TRAIT] 2.475e-01 3.290e-01 0.752 0.453
Age -2.622e-02 3.651e-02 -0.718 0.473
Gendermale 7.406e-01 6.851e-01 1.081 0.281
ORdate_epoch 1.617e-03 8.314e-04 1.946 0.053 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.409 on 221 degrees of freedom
Multiple R-squared: 0.02426, Adjusted R-squared: 0.006601
F-statistic: 1.374 on 4 and 221 DF, p-value: 0.2439
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: RANTES_rank
Effect size...............: 0.247523
Standard error............: 0.328956
Odds ratio (effect size)..: 1.281
Lower 95% CI..............: 0.672
Upper 95% CI..............: 2.441
T-value...................: 0.752451
P-value...................: 0.4525806
R^2.......................: 0.024262
Adjusted r^2..............: 0.006601
Sample size of AE DB......: 622
Sample size of model......: 226
Missing data %............: 63.6656
- processing MIG_rankfilter: removed 395 rows (64%), 227 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-14.542930 0.001323
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.8434 -2.1050 -1.2442 0.0958 30.2816
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.109e+01 1.013e+01 -1.094 0.275
currentDF[, TRAIT] 3.188e-01 3.109e-01 1.025 0.306
Age -2.538e-02 3.611e-02 -0.703 0.483
Gendermale 6.862e-01 6.806e-01 1.008 0.314
ORdate_epoch 1.144e-03 7.904e-04 1.448 0.149
Residual standard error: 4.396 on 222 degrees of freedom
Multiple R-squared: 0.02698, Adjusted R-squared: 0.009448
F-statistic: 1.539 on 4 and 222 DF, p-value: 0.1919
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MIG_rank
Effect size...............: 0.318818
Standard error............: 0.310908
Odds ratio (effect size)..: 1.376
Lower 95% CI..............: 0.748
Upper 95% CI..............: 2.53
T-value...................: 1.02544
P-value...................: 0.3062716
R^2.......................: 0.02698
Adjusted r^2..............: 0.009448
Sample size of AE DB......: 622
Sample size of model......: 227
Missing data %............: 63.50482
- processing IP10_rankfilter: removed 415 rows (67%), 207 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-14.74815 0.64339 0.00135
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.6412 -2.1686 -1.2411 0.2919 30.1504
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.510e+01 1.042e+01 -1.449 0.1489
currentDF[, TRAIT] 6.161e-01 3.209e-01 1.920 0.0563 .
Age -1.884e-02 3.918e-02 -0.481 0.6311
Gendermale 9.079e-01 7.154e-01 1.269 0.2059
ORdate_epoch 1.426e-03 7.981e-04 1.787 0.0755 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.533 on 202 degrees of freedom
Multiple R-squared: 0.04241, Adjusted R-squared: 0.02345
F-statistic: 2.237 on 4 and 202 DF, p-value: 0.06638
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IP10_rank
Effect size...............: 0.616118
Standard error............: 0.320928
Odds ratio (effect size)..: 1.852
Lower 95% CI..............: 0.987
Upper 95% CI..............: 3.473
T-value...................: 1.919802
P-value...................: 0.05629171
R^2.......................: 0.042411
Adjusted r^2..............: 0.023449
Sample size of AE DB......: 622
Sample size of model......: 207
Missing data %............: 66.72026
- processing Eotaxin1_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-14.519110 0.001321
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4142 -1.9931 -1.2138 -0.0131 30.6133
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.354e+01 9.848e+00 -1.375 0.171
currentDF[, TRAIT] 2.062e-02 2.949e-01 0.070 0.944
Age -3.150e-02 3.553e-02 -0.887 0.376
Gendermale 7.234e-01 6.731e-01 1.075 0.284
ORdate_epoch 1.370e-03 7.605e-04 1.802 0.073 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.381 on 225 degrees of freedom
Multiple R-squared: 0.02221, Adjusted R-squared: 0.004829
F-statistic: 1.278 on 4 and 225 DF, p-value: 0.2795
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.020624
Standard error............: 0.294942
Odds ratio (effect size)..: 1.021
Lower 95% CI..............: 0.573
Upper 95% CI..............: 1.82
T-value...................: 0.069927
P-value...................: 0.9443139
R^2.......................: 0.022212
Adjusted r^2..............: 0.004829
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing TARC_rankfilter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
2.256
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5768 -2.1300 -1.3717 -0.0275 30.4690
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.112e+01 1.315e+01 -0.846 0.399
currentDF[, TRAIT] 8.094e-02 3.536e-01 0.229 0.819
Age -3.457e-02 4.055e-02 -0.852 0.395
Gendermale 8.457e-01 7.618e-01 1.110 0.268
ORdate_epoch 1.195e-03 9.933e-04 1.203 0.230
Residual standard error: 4.637 on 198 degrees of freedom
Multiple R-squared: 0.01726, Adjusted R-squared: -0.002597
F-statistic: 0.8692 on 4 and 198 DF, p-value: 0.4834
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: TARC_rank
Effect size...............: 0.080943
Standard error............: 0.353617
Odds ratio (effect size)..: 1.084
Lower 95% CI..............: 0.542
Upper 95% CI..............: 2.168
T-value...................: 0.2289
P-value...................: 0.8191829
R^2.......................: 0.017256
Adjusted r^2..............: -0.002597
Sample size of AE DB......: 622
Sample size of model......: 203
Missing data %............: 67.36334
- processing PARC_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-20.837956 0.578566 0.001825
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.0767 -2.0612 -1.1306 0.0867 30.6517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.059e+01 1.031e+01 -1.997 0.0470 *
currentDF[, TRAIT] 5.945e-01 3.067e-01 1.938 0.0539 .
Age -2.711e-02 3.525e-02 -0.769 0.4428
Gendermale 8.345e-01 6.670e-01 1.251 0.2122
ORdate_epoch 1.901e-03 7.919e-04 2.400 0.0172 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.345 on 225 degrees of freedom
Multiple R-squared: 0.03824, Adjusted R-squared: 0.02115
F-statistic: 2.237 on 4 and 225 DF, p-value: 0.06596
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: PARC_rank
Effect size...............: 0.594455
Standard error............: 0.306749
Odds ratio (effect size)..: 1.812
Lower 95% CI..............: 0.993
Upper 95% CI..............: 3.306
T-value...................: 1.937918
P-value...................: 0.05388457
R^2.......................: 0.038244
Adjusted r^2..............: 0.021146
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing MDC_rankfilter: removed 407 rows (65%), 215 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-13.696173 0.001261
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.357 -2.085 -1.206 -0.141 30.611
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.167e+01 1.071e+01 -1.090 0.277
currentDF[, TRAIT] -1.240e-01 3.258e-01 -0.381 0.704
Age -3.240e-02 3.785e-02 -0.856 0.393
Gendermale 7.928e-01 7.156e-01 1.108 0.269
ORdate_epoch 1.226e-03 8.204e-04 1.495 0.137
Residual standard error: 4.511 on 210 degrees of freedom
Multiple R-squared: 0.02196, Adjusted R-squared: 0.003331
F-statistic: 1.179 on 4 and 210 DF, p-value: 0.3212
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MDC_rank
Effect size...............: -0.124021
Standard error............: 0.325763
Odds ratio (effect size)..: 0.883
Lower 95% CI..............: 0.466
Upper 95% CI..............: 1.673
T-value...................: -0.380708
P-value...................: 0.7038051
R^2.......................: 0.02196
Adjusted r^2..............: 0.003331
Sample size of AE DB......: 622
Sample size of model......: 215
Missing data %............: 65.43408
- processing OPG_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-14.519110 0.001321
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5504 -2.0188 -1.2085 0.0468 30.6624
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.361e+01 9.743e+00 -1.396 0.1640
currentDF[, TRAIT] 1.050e-01 2.922e-01 0.359 0.7198
Age -3.013e-02 3.571e-02 -0.844 0.3998
Gendermale 7.163e-01 6.708e-01 1.068 0.2868
ORdate_epoch 1.368e-03 7.511e-04 1.822 0.0698 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.38 on 225 degrees of freedom
Multiple R-squared: 0.02275, Adjusted R-squared: 0.005378
F-statistic: 1.31 on 4 and 225 DF, p-value: 0.2673
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: OPG_rank
Effect size...............: 0.104952
Standard error............: 0.292183
Odds ratio (effect size)..: 1.111
Lower 95% CI..............: 0.626
Upper 95% CI..............: 1.969
T-value...................: 0.359197
P-value...................: 0.7197843
R^2.......................: 0.022751
Adjusted r^2..............: 0.005378
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing sICAM1_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-14.519110 0.001321
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4487 -1.9827 -1.1965 -0.0319 30.6501
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.231e+01 1.005e+01 -1.225 0.2219
currentDF[, TRAIT] -1.599e-01 2.999e-01 -0.533 0.5945
Age -3.441e-02 3.582e-02 -0.961 0.3378
Gendermale 7.117e-01 6.705e-01 1.061 0.2896
ORdate_epoch 1.289e-03 7.689e-04 1.676 0.0951 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.378 on 225 degrees of freedom
Multiple R-squared: 0.02342, Adjusted R-squared: 0.006063
F-statistic: 1.349 on 4 and 225 DF, p-value: 0.2526
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: sICAM1_rank
Effect size...............: -0.159873
Standard error............: 0.299935
Odds ratio (effect size)..: 0.852
Lower 95% CI..............: 0.473
Upper 95% CI..............: 1.534
T-value...................: -0.533025
P-value...................: 0.5945424
R^2.......................: 0.023424
Adjusted r^2..............: 0.006063
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing VEGFA_rankfilter: removed 421 rows (68%), 201 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-13.17855 0.00122
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5095 -2.0618 -1.2743 -0.0773 30.6924
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.569e+01 1.211e+01 -1.296 0.196
currentDF[, TRAIT] -1.890e-01 3.583e-01 -0.527 0.598
Age -2.861e-02 3.971e-02 -0.721 0.472
Gendermale 5.617e-01 7.408e-01 0.758 0.449
ORdate_epoch 1.541e-03 9.516e-04 1.620 0.107
Residual standard error: 4.549 on 196 degrees of freedom
Multiple R-squared: 0.01685, Adjusted R-squared: -0.003216
F-statistic: 0.8397 on 4 and 196 DF, p-value: 0.5015
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: VEGFA_rank
Effect size...............: -0.188959
Standard error............: 0.358267
Odds ratio (effect size)..: 0.828
Lower 95% CI..............: 0.41
Upper 95% CI..............: 1.671
T-value...................: -0.527426
P-value...................: 0.5984946
R^2.......................: 0.016848
Adjusted r^2..............: -0.003216
Sample size of AE DB......: 622
Sample size of model......: 201
Missing data %............: 67.68489
- processing TGFB_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-15.40295 0.00139
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3750 -1.9905 -1.2464 0.0107 30.6552
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.431e+01 9.749e+00 -1.468 0.1435
currentDF[, TRAIT] -1.262e-01 2.926e-01 -0.431 0.6666
Age -2.329e-02 3.527e-02 -0.660 0.5097
Gendermale 5.968e-01 6.569e-01 0.908 0.3646
ORdate_epoch 1.394e-03 7.529e-04 1.851 0.0654 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.345 on 226 degrees of freedom
Multiple R-squared: 0.02174, Adjusted R-squared: 0.004424
F-statistic: 1.256 on 4 and 226 DF, p-value: 0.2884
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: TGFB_rank
Effect size...............: -0.126227
Standard error............: 0.292592
Odds ratio (effect size)..: 0.881
Lower 95% CI..............: 0.497
Upper 95% CI..............: 1.564
T-value...................: -0.431409
P-value...................: 0.6665824
R^2.......................: 0.021739
Adjusted r^2..............: 0.004424
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP2_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-16.834255 0.001508
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4292 -1.9395 -1.1981 0.0116 30.3463
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.405e+01 9.874e+00 -1.423 0.1562
currentDF[, TRAIT] -2.517e-01 2.937e-01 -0.857 0.3923
Age -3.660e-02 3.498e-02 -1.046 0.2965
Gendermale 5.305e-01 6.612e-01 0.802 0.4232
ORdate_epoch 1.452e-03 7.688e-04 1.889 0.0602 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.327 on 226 degrees of freedom
Multiple R-squared: 0.02826, Adjusted R-squared: 0.01106
F-statistic: 1.643 on 4 and 226 DF, p-value: 0.1643
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MMP2_rank
Effect size...............: -0.251714
Standard error............: 0.29366
Odds ratio (effect size)..: 0.777
Lower 95% CI..............: 0.437
Upper 95% CI..............: 1.382
T-value...................: -0.857161
P-value...................: 0.3922639
R^2.......................: 0.028263
Adjusted r^2..............: 0.011064
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP8_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-16.834255 0.001508
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5421 -1.9580 -1.1950 0.1059 30.1261
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.538e+01 9.715e+00 -1.583 0.1148
currentDF[, TRAIT] 2.445e-01 2.875e-01 0.850 0.3960
Age -3.338e-02 3.485e-02 -0.958 0.3391
Gendermale 5.559e-01 6.573e-01 0.846 0.3986
ORdate_epoch 1.539e-03 7.597e-04 2.026 0.0439 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.328 on 226 degrees of freedom
Multiple R-squared: 0.02821, Adjusted R-squared: 0.01101
F-statistic: 1.64 on 4 and 226 DF, p-value: 0.165
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MMP8_rank
Effect size...............: 0.244472
Standard error............: 0.287467
Odds ratio (effect size)..: 1.277
Lower 95% CI..............: 0.727
Upper 95% CI..............: 2.243
T-value...................: 0.850438
P-value...................: 0.3959819
R^2.......................: 0.028214
Adjusted r^2..............: 0.011014
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP9_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-18.416484 0.435535 0.001634
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.8374 -2.0169 -1.1709 0.1997 29.7487
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.720e+01 9.738e+00 -1.766 0.0787 .
currentDF[, TRAIT] 4.332e-01 2.863e-01 1.513 0.1316
Age -3.317e-02 3.472e-02 -0.955 0.3405
Gendermale 6.247e-01 6.509e-01 0.960 0.3382
ORdate_epoch 1.679e-03 7.614e-04 2.206 0.0284 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.313 on 226 degrees of freedom
Multiple R-squared: 0.03488, Adjusted R-squared: 0.0178
F-statistic: 2.042 on 4 and 226 DF, p-value: 0.08943
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MMP9_rank
Effect size...............: 0.433206
Standard error............: 0.286292
Odds ratio (effect size)..: 1.542
Lower 95% CI..............: 0.88
Upper 95% CI..............: 2.703
T-value...................: 1.513162
P-value...................: 0.1316357
R^2.......................: 0.034882
Adjusted r^2..............: 0.0178
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
Analysis of PCSK9.
- processing IL2_rankfilter: removed 440 rows (71%), 182 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
9.1541829 -0.0006487
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.7997 -1.1141 -0.6010 0.3752 11.7523
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.6867612 5.2073117 2.052 0.0416 *
currentDF[, TRAIT] -0.1399590 0.1445148 -0.968 0.3341
Age -0.0130821 0.0174453 -0.750 0.4543
Gendermale 0.1986290 0.3384235 0.587 0.5580
ORdate_epoch -0.0007128 0.0004119 -1.730 0.0853 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.91 on 177 degrees of freedom
Multiple R-squared: 0.02459, Adjusted R-squared: 0.002545
F-statistic: 1.115 on 4 and 177 DF, p-value: 0.3508
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL2_rank
Effect size...............: -0.139959
Standard error............: 0.144515
Odds ratio (effect size)..: 0.869
Lower 95% CI..............: 0.655
Upper 95% CI..............: 1.154
T-value...................: -0.968475
P-value...................: 0.3341286
R^2.......................: 0.024588
Adjusted r^2..............: 0.002545
Sample size of AE DB......: 622
Sample size of model......: 182
Missing data %............: 70.73955
- processing IL4_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
9.6404719 -0.0006855
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.7020 -1.1374 -0.5469 0.3174 11.5282
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.3407209 5.9349710 1.911 0.0578 .
currentDF[, TRAIT] -0.0817999 0.1606905 -0.509 0.6114
Age -0.0182960 0.0192327 -0.951 0.3429
Gendermale 0.2853139 0.3772642 0.756 0.4506
ORdate_epoch -0.0007401 0.0004710 -1.571 0.1181
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.991 on 159 degrees of freedom
Multiple R-squared: 0.02357, Adjusted R-squared: -0.0009991
F-statistic: 0.9593 on 4 and 159 DF, p-value: 0.4316
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL4_rank
Effect size...............: -0.0818
Standard error............: 0.160691
Odds ratio (effect size)..: 0.921
Lower 95% CI..............: 0.672
Upper 95% CI..............: 1.263
T-value...................: -0.509052
P-value...................: 0.6114211
R^2.......................: 0.023565
Adjusted r^2..............: -0.000999
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing IL5_rankfilter: removed 443 rows (71%), 179 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
11.6334487 -0.0008481
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.8517 -1.0651 -0.5250 0.3153 11.5924
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.4143680 5.4162244 2.477 0.0142 *
currentDF[, TRAIT] -0.1381720 0.1477893 -0.935 0.3511
Age -0.0208367 0.0178587 -1.167 0.2449
Gendermale 0.3078135 0.3470472 0.887 0.3763
ORdate_epoch -0.0008968 0.0004341 -2.066 0.0403 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.928 on 174 degrees of freedom
Multiple R-squared: 0.03763, Adjusted R-squared: 0.01551
F-statistic: 1.701 on 4 and 174 DF, p-value: 0.1519
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL5_rank
Effect size...............: -0.138172
Standard error............: 0.147789
Odds ratio (effect size)..: 0.871
Lower 95% CI..............: 0.652
Upper 95% CI..............: 1.164
T-value...................: -0.934926
P-value...................: 0.3511223
R^2.......................: 0.03763
Adjusted r^2..............: 0.015506
Sample size of AE DB......: 622
Sample size of model......: 179
Missing data %............: 71.22186
- processing IL6_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
1.0899 -0.2773
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.7903 -1.0887 -0.4857 0.3843 11.8563
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.2459711 4.6722596 1.765 0.0792 .
currentDF[, TRAIT] -0.2588347 0.1393575 -1.857 0.0649 .
Age -0.0198254 0.0168730 -1.175 0.2415
Gendermale 0.1425333 0.3144809 0.453 0.6509
ORdate_epoch -0.0004749 0.0003729 -1.273 0.2045
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.864 on 184 degrees of freedom
Multiple R-squared: 0.03954, Adjusted R-squared: 0.01866
F-statistic: 1.894 on 4 and 184 DF, p-value: 0.1134
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL6_rank
Effect size...............: -0.258835
Standard error............: 0.139358
Odds ratio (effect size)..: 0.772
Lower 95% CI..............: 0.587
Upper 95% CI..............: 1.014
T-value...................: -1.857343
P-value...................: 0.06486054
R^2.......................: 0.039537
Adjusted r^2..............: 0.018657
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing IL8_rankfilter: removed 442 rows (71%), 180 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
9.6599386 -0.0006799
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.9041 -1.1647 -0.4570 0.3999 11.2083
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.356649 5.031639 2.257 0.0252 *
currentDF[, TRAIT] 0.117346 0.150181 0.781 0.4356
Age -0.014951 0.017185 -0.870 0.3855
Gendermale 0.117391 0.341964 0.343 0.7318
ORdate_epoch -0.000742 0.000400 -1.855 0.0652 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.925 on 175 degrees of freedom
Multiple R-squared: 0.02632, Adjusted R-squared: 0.004061
F-statistic: 1.182 on 4 and 175 DF, p-value: 0.3202
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL8_rank
Effect size...............: 0.117346
Standard error............: 0.150181
Odds ratio (effect size)..: 1.125
Lower 95% CI..............: 0.838
Upper 95% CI..............: 1.509
T-value...................: 0.781363
P-value...................: 0.4356446
R^2.......................: 0.026317
Adjusted r^2..............: 0.004061
Sample size of AE DB......: 622
Sample size of model......: 180
Missing data %............: 71.06109
- processing IL9_rankfilter: removed 412 rows (66%), 210 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
2.79897 -0.02431
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.8385 -1.1789 -0.4332 0.5509 11.2748
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.9306212 4.1754537 1.899 0.0589 .
currentDF[, TRAIT] 0.1194181 0.1284175 0.930 0.3535
Age -0.0226491 0.0158432 -1.430 0.1544
Gendermale 0.0753740 0.2952192 0.255 0.7987
ORdate_epoch -0.0004216 0.0003197 -1.319 0.1887
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.843 on 205 degrees of freedom
Multiple R-squared: 0.02499, Adjusted R-squared: 0.005965
F-statistic: 1.314 on 4 and 205 DF, p-value: 0.2661
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL9_rank
Effect size...............: 0.119418
Standard error............: 0.128418
Odds ratio (effect size)..: 1.127
Lower 95% CI..............: 0.876
Upper 95% CI..............: 1.449
T-value...................: 0.92992
P-value...................: 0.3535061
R^2.......................: 0.024989
Adjusted r^2..............: 0.005965
Sample size of AE DB......: 622
Sample size of model......: 210
Missing data %............: 66.23794
- processing IL10_rankfilter: removed 465 rows (75%), 157 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
10.8438119 -0.0007837
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.7342 -1.1622 -0.5555 0.2723 11.5111
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.1676316 6.4097410 2.054 0.0417 *
currentDF[, TRAIT] -0.1138161 0.1687950 -0.674 0.5012
Age -0.0188995 0.0200846 -0.941 0.3482
Gendermale 0.2384553 0.3891907 0.613 0.5410
ORdate_epoch -0.0008828 0.0005031 -1.755 0.0813 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.028 on 152 degrees of freedom
Multiple R-squared: 0.02643, Adjusted R-squared: 0.0008145
F-statistic: 1.032 on 4 and 152 DF, p-value: 0.3928
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL10_rank
Effect size...............: -0.113816
Standard error............: 0.168795
Odds ratio (effect size)..: 0.892
Lower 95% CI..............: 0.641
Upper 95% CI..............: 1.242
T-value...................: -0.674286
P-value...................: 0.5011535
R^2.......................: 0.026435
Adjusted r^2..............: 0.000814
Sample size of AE DB......: 622
Sample size of model......: 157
Missing data %............: 74.75884
- processing IL12_rankfilter: removed 456 rows (73%), 166 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
11.3951334 -0.0008278
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.8102 -1.1174 -0.5299 0.3320 11.4146
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.4623892 5.9089279 2.278 0.0240 *
currentDF[, TRAIT] -0.0749530 0.1578944 -0.475 0.6356
Age -0.0203907 0.0191307 -1.066 0.2881
Gendermale 0.3143598 0.3685102 0.853 0.3949
ORdate_epoch -0.0009019 0.0004678 -1.928 0.0556 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.976 on 161 degrees of freedom
Multiple R-squared: 0.03099, Adjusted R-squared: 0.006913
F-statistic: 1.287 on 4 and 161 DF, p-value: 0.2773
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL12_rank
Effect size...............: -0.074953
Standard error............: 0.157894
Odds ratio (effect size)..: 0.928
Lower 95% CI..............: 0.681
Upper 95% CI..............: 1.264
T-value...................: -0.474703
P-value...................: 0.6356417
R^2.......................: 0.030988
Adjusted r^2..............: 0.006913
Sample size of AE DB......: 622
Sample size of model......: 166
Missing data %............: 73.3119
- processing IL13_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) Age ORdate_epoch
8.2356131 -0.0214781 -0.0004477
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.6806 -1.1517 -0.4245 0.4343 11.3076
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.2746374 4.0421179 2.047 0.0418 *
currentDF[, TRAIT] 0.0772504 0.1214434 0.636 0.5254
Age -0.0203337 0.0147919 -1.375 0.1706
Gendermale 0.0522362 0.2781823 0.188 0.8512
ORdate_epoch -0.0004601 0.0003120 -1.475 0.1417
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.814 on 225 degrees of freedom
Multiple R-squared: 0.02063, Adjusted R-squared: 0.003218
F-statistic: 1.185 on 4 and 225 DF, p-value: 0.3183
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL13_rank
Effect size...............: 0.07725
Standard error............: 0.121443
Odds ratio (effect size)..: 1.08
Lower 95% CI..............: 0.851
Upper 95% CI..............: 1.371
T-value...................: 0.636102
P-value...................: 0.5253564
R^2.......................: 0.020629
Adjusted r^2..............: 0.003218
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing IL21_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) Age ORdate_epoch
8.2356131 -0.0214781 -0.0004477
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.7033 -1.1692 -0.4272 0.4686 11.2812
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.2404724 4.0383103 2.041 0.0425 *
currentDF[, TRAIT] 0.0837819 0.1210152 0.692 0.4894
Age -0.0205102 0.0147481 -1.391 0.1657
Gendermale 0.0484180 0.2784293 0.174 0.8621
ORdate_epoch -0.0004562 0.0003114 -1.465 0.1443
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.814 on 225 degrees of freedom
Multiple R-squared: 0.02095, Adjusted R-squared: 0.003548
F-statistic: 1.204 on 4 and 225 DF, p-value: 0.31
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL21_rank
Effect size...............: 0.083782
Standard error............: 0.121015
Odds ratio (effect size)..: 1.087
Lower 95% CI..............: 0.858
Upper 95% CI..............: 1.378
T-value...................: 0.692325
P-value...................: 0.4894468
R^2.......................: 0.020953
Adjusted r^2..............: 0.003548
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing INFG_rankfilter: removed 449 rows (72%), 173 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
10.3147192 -0.0007374
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.8319 -1.1394 -0.5288 0.3587 11.3976
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.3218069 5.6080023 2.197 0.0294 *
currentDF[, TRAIT] -0.0527830 0.1617470 -0.326 0.7446
Age -0.0196304 0.0185450 -1.059 0.2913
Gendermale 0.2705697 0.3694210 0.732 0.4649
ORdate_epoch -0.0008088 0.0004412 -1.833 0.0685 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.962 on 168 degrees of freedom
Multiple R-squared: 0.02829, Adjusted R-squared: 0.005151
F-statistic: 1.223 on 4 and 168 DF, p-value: 0.3031
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: INFG_rank
Effect size...............: -0.052783
Standard error............: 0.161747
Odds ratio (effect size)..: 0.949
Lower 95% CI..............: 0.691
Upper 95% CI..............: 1.302
T-value...................: -0.32633
P-value...................: 0.7445806
R^2.......................: 0.028287
Adjusted r^2..............: 0.005151
Sample size of AE DB......: 622
Sample size of model......: 173
Missing data %............: 72.18649
- processing TNFA_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
11.3929054 -0.0008265
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.7874 -1.1013 -0.5387 0.3471 11.4889
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.9241512 5.8974847 2.191 0.0299 *
currentDF[, TRAIT] -0.0745333 0.1577660 -0.472 0.6373
Age -0.0179412 0.0192921 -0.930 0.3538
Gendermale 0.3363122 0.3661845 0.918 0.3598
ORdate_epoch -0.0008722 0.0004650 -1.875 0.0626 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.988 on 159 degrees of freedom
Multiple R-squared: 0.03068, Adjusted R-squared: 0.00629
F-statistic: 1.258 on 4 and 159 DF, p-value: 0.2889
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: TNFA_rank
Effect size...............: -0.074533
Standard error............: 0.157766
Odds ratio (effect size)..: 0.928
Lower 95% CI..............: 0.681
Upper 95% CI..............: 1.265
T-value...................: -0.472429
P-value...................: 0.6372681
R^2.......................: 0.030675
Adjusted r^2..............: 0.00629
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing MIF_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
1.1565 0.2603
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.8792 -1.1237 -0.4763 0.4106 11.1418
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.8297210 4.4943802 1.075 0.284
currentDF[, TRAIT] 0.2193344 0.1339830 1.637 0.103
Age -0.0203267 0.0146314 -1.389 0.166
Gendermale 0.0713649 0.2762382 0.258 0.796
ORdate_epoch -0.0001870 0.0003468 -0.539 0.590
Residual standard error: 1.805 on 225 degrees of freedom
Multiple R-squared: 0.03042, Adjusted R-squared: 0.01318
F-statistic: 1.765 on 4 and 225 DF, p-value: 0.1369
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MIF_rank
Effect size...............: 0.219334
Standard error............: 0.133983
Odds ratio (effect size)..: 1.245
Lower 95% CI..............: 0.958
Upper 95% CI..............: 1.619
T-value...................: 1.637031
P-value...................: 0.1030218
R^2.......................: 0.030416
Adjusted r^2..............: 0.013179
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing MCP1_rankfilter: removed 394 rows (63%), 228 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
7.0892859 -0.0004715
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.862 -1.120 -0.423 0.404 11.369
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.5157309 4.1426209 1.814 0.071 .
currentDF[, TRAIT] 0.1095011 0.1240198 0.883 0.378
Age -0.0191122 0.0149044 -1.282 0.201
Gendermale 0.0340664 0.2806999 0.121 0.904
ORdate_epoch -0.0004044 0.0003183 -1.270 0.205
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.818 on 223 degrees of freedom
Multiple R-squared: 0.0219, Adjusted R-squared: 0.004358
F-statistic: 1.248 on 4 and 223 DF, p-value: 0.2914
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MCP1_rank
Effect size...............: 0.109501
Standard error............: 0.12402
Odds ratio (effect size)..: 1.116
Lower 95% CI..............: 0.875
Upper 95% CI..............: 1.423
T-value...................: 0.882932
P-value...................: 0.3782236
R^2.......................: 0.021903
Adjusted r^2..............: 0.004358
Sample size of AE DB......: 622
Sample size of model......: 228
Missing data %............: 63.34405
- processing MIP1a_rankfilter: removed 408 rows (66%), 214 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
2.64159 -0.02216
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.7388 -1.1640 -0.4069 0.4387 11.2726
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.9169097 4.1229287 1.920 0.0562 .
currentDF[, TRAIT] 0.1023908 0.1259402 0.813 0.4171
Age -0.0214162 0.0153191 -1.398 0.1636
Gendermale 0.0501928 0.2930888 0.171 0.8642
ORdate_epoch -0.0004268 0.0003168 -1.347 0.1794
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.831 on 209 degrees of freedom
Multiple R-squared: 0.02159, Adjusted R-squared: 0.002867
F-statistic: 1.153 on 4 and 209 DF, p-value: 0.3327
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.102391
Standard error............: 0.12594
Odds ratio (effect size)..: 1.108
Lower 95% CI..............: 0.865
Upper 95% CI..............: 1.418
T-value...................: 0.813011
P-value...................: 0.4171367
R^2.......................: 0.021593
Adjusted r^2..............: 0.002867
Sample size of AE DB......: 622
Sample size of model......: 214
Missing data %............: 65.59485
- processing RANTES_rankfilter: removed 396 rows (64%), 226 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) Age ORdate_epoch
8.2010798 -0.0208171 -0.0004485
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.7193 -1.1356 -0.4676 0.4209 11.1652
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.5588814 4.5372531 1.886 0.0606 .
currentDF[, TRAIT] -0.0280877 0.1363121 -0.206 0.8369
Age -0.0214415 0.0151282 -1.417 0.1578
Gendermale 0.0302855 0.2838968 0.107 0.9151
ORdate_epoch -0.0004755 0.0003445 -1.380 0.1689
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.827 on 221 degrees of freedom
Multiple R-squared: 0.01838, Adjusted R-squared: 0.00061
F-statistic: 1.034 on 4 and 221 DF, p-value: 0.3903
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: RANTES_rank
Effect size...............: -0.028088
Standard error............: 0.136312
Odds ratio (effect size)..: 0.972
Lower 95% CI..............: 0.744
Upper 95% CI..............: 1.27
T-value...................: -0.206055
P-value...................: 0.836938
R^2.......................: 0.018377
Adjusted r^2..............: 0.00061
Sample size of AE DB......: 622
Sample size of model......: 226
Missing data %............: 63.6656
- processing MIG_rankfilter: removed 395 rows (64%), 227 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) Age ORdate_epoch
8.1935014 -0.0220026 -0.0004427
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.7752 -1.1603 -0.4243 0.4936 11.2843
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.2902521 4.1605284 2.233 0.0266 *
currentDF[, TRAIT] 0.1390263 0.1276657 1.089 0.2773
Age -0.0192866 0.0148284 -1.301 0.1947
Gendermale 0.0129311 0.2794631 0.046 0.9631
ORdate_epoch -0.0005455 0.0003246 -1.681 0.0942 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.805 on 222 degrees of freedom
Multiple R-squared: 0.02456, Adjusted R-squared: 0.006987
F-statistic: 1.398 on 4 and 222 DF, p-value: 0.2357
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MIG_rank
Effect size...............: 0.139026
Standard error............: 0.127666
Odds ratio (effect size)..: 1.149
Lower 95% CI..............: 0.895
Upper 95% CI..............: 1.476
T-value...................: 1.088987
P-value...................: 0.2773405
R^2.......................: 0.024562
Adjusted r^2..............: 0.006987
Sample size of AE DB......: 622
Sample size of model......: 227
Missing data %............: 63.50482
- processing IP10_rankfilter: removed 415 rows (67%), 207 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
2.94988 -0.02668
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.8386 -1.1099 -0.3646 0.3677 11.2761
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.130193 4.191749 1.701 0.0905 .
currentDF[, TRAIT] 0.020848 0.129070 0.162 0.8718
Age -0.026232 0.015757 -1.665 0.0975 .
Gendermale 0.097008 0.287705 0.337 0.7363
ORdate_epoch -0.000341 0.000321 -1.062 0.2893
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.823 on 202 degrees of freedom
Multiple R-squared: 0.02076, Adjusted R-squared: 0.001371
F-statistic: 1.071 on 4 and 202 DF, p-value: 0.3721
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IP10_rank
Effect size...............: 0.020848
Standard error............: 0.12907
Odds ratio (effect size)..: 1.021
Lower 95% CI..............: 0.793
Upper 95% CI..............: 1.315
T-value...................: 0.161526
P-value...................: 0.8718405
R^2.......................: 0.020761
Adjusted r^2..............: 0.001371
Sample size of AE DB......: 622
Sample size of model......: 207
Missing data %............: 66.72026
- processing Eotaxin1_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) Age ORdate_epoch
8.2356131 -0.0214781 -0.0004477
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.7632 -1.1644 -0.4426 0.4722 11.2333
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.5696251 4.0773889 2.102 0.0367 *
currentDF[, TRAIT] 0.0905360 0.1221154 0.741 0.4592
Age -0.0207770 0.0147117 -1.412 0.1592
Gendermale 0.0446076 0.2787027 0.160 0.8730
ORdate_epoch -0.0004807 0.0003149 -1.527 0.1282
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.814 on 225 degrees of freedom
Multiple R-squared: 0.02126, Adjusted R-squared: 0.003859
F-statistic: 1.222 on 4 and 225 DF, p-value: 0.3024
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.090536
Standard error............: 0.122115
Odds ratio (effect size)..: 1.095
Lower 95% CI..............: 0.862
Upper 95% CI..............: 1.391
T-value...................: 0.741397
P-value...................: 0.4592259
R^2.......................: 0.021259
Adjusted r^2..............: 0.003859
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing TARC_rankfilter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale
0.8051 0.1672 0.3894
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.6552 -1.0730 -0.4158 0.5553 8.7697
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.670e+00 4.748e+00 0.352 0.725
currentDF[, TRAIT] 1.596e-01 1.277e-01 1.250 0.213
Age 1.933e-03 1.464e-02 0.132 0.895
Gendermale 3.852e-01 2.750e-01 1.400 0.163
ORdate_epoch -7.866e-05 3.586e-04 -0.219 0.827
Residual standard error: 1.674 on 198 degrees of freedom
Multiple R-squared: 0.01939, Adjusted R-squared: -0.0004253
F-statistic: 0.9785 on 4 and 198 DF, p-value: 0.4203
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: TARC_rank
Effect size...............: 0.159618
Standard error............: 0.127674
Odds ratio (effect size)..: 1.173
Lower 95% CI..............: 0.913
Upper 95% CI..............: 1.507
T-value...................: 1.250192
P-value...................: 0.2127049
R^2.......................: 0.019385
Adjusted r^2..............: -0.000425
Sample size of AE DB......: 622
Sample size of model......: 203
Missing data %............: 67.36334
- processing PARC_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) Age ORdate_epoch
8.2356131 -0.0214781 -0.0004477
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.7946 -1.1260 -0.4183 0.4067 11.3391
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.3076890 4.3058617 1.697 0.0911 .
currentDF[, TRAIT] 0.0706332 0.1281280 0.551 0.5820
Age -0.0209004 0.0147251 -1.419 0.1572
Gendermale 0.0764582 0.2786028 0.274 0.7840
ORdate_epoch -0.0003815 0.0003308 -1.153 0.2500
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.815 on 225 degrees of freedom
Multiple R-squared: 0.02019, Adjusted R-squared: 0.002772
F-statistic: 1.159 on 4 and 225 DF, p-value: 0.3297
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: PARC_rank
Effect size...............: 0.070633
Standard error............: 0.128128
Odds ratio (effect size)..: 1.073
Lower 95% CI..............: 0.835
Upper 95% CI..............: 1.38
T-value...................: 0.551271
P-value...................: 0.5819951
R^2.......................: 0.020191
Adjusted r^2..............: 0.002772
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing MDC_rankfilter: removed 407 rows (65%), 215 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
1.1349 0.2423
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.9500 -1.1275 -0.4380 0.5649 11.2986
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.8576725 4.3265126 1.354 0.177
currentDF[, TRAIT] 0.1957865 0.1316427 1.487 0.138
Age -0.0205178 0.0152965 -1.341 0.181
Gendermale 0.0923191 0.2891587 0.319 0.750
ORdate_epoch -0.0002710 0.0003315 -0.817 0.415
Residual standard error: 1.823 on 210 degrees of freedom
Multiple R-squared: 0.0294, Adjusted R-squared: 0.01092
F-statistic: 1.59 on 4 and 210 DF, p-value: 0.178
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MDC_rank
Effect size...............: 0.195787
Standard error............: 0.131643
Odds ratio (effect size)..: 1.216
Lower 95% CI..............: 0.94
Upper 95% CI..............: 1.574
T-value...................: 1.487257
P-value...................: 0.138447
R^2.......................: 0.029403
Adjusted r^2..............: 0.010916
Sample size of AE DB......: 622
Sample size of model......: 215
Missing data %............: 65.43408
- processing OPG_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) Age ORdate_epoch
8.2356131 -0.0214781 -0.0004477
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.7537 -1.1427 -0.3975 0.3883 11.1499
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.1229745 4.0390876 2.011 0.0455 *
currentDF[, TRAIT] -0.0344514 0.1211326 -0.284 0.7764
Age -0.0219421 0.0148051 -1.482 0.1397
Gendermale 0.0675602 0.2781073 0.243 0.8083
ORdate_epoch -0.0004403 0.0003114 -1.414 0.1588
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.816 on 225 degrees of freedom
Multiple R-squared: 0.01922, Adjusted R-squared: 0.001784
F-statistic: 1.102 on 4 and 225 DF, p-value: 0.3563
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: OPG_rank
Effect size...............: -0.034451
Standard error............: 0.121133
Odds ratio (effect size)..: 0.966
Lower 95% CI..............: 0.762
Upper 95% CI..............: 1.225
T-value...................: -0.284411
P-value...................: 0.776357
R^2.......................: 0.01922
Adjusted r^2..............: 0.001784
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing sICAM1_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) Age ORdate_epoch
8.2356131 -0.0214781 -0.0004477
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.8209 -1.1109 -0.4214 0.3886 11.2455
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.4829984 4.1653632 1.796 0.0738 .
currentDF[, TRAIT] 0.0785145 0.1243014 0.632 0.5283
Age -0.0200872 0.0148448 -1.353 0.1774
Gendermale 0.0716794 0.2778749 0.258 0.7967
ORdate_epoch -0.0003996 0.0003187 -1.254 0.2112
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.814 on 225 degrees of freedom
Multiple R-squared: 0.0206, Adjusted R-squared: 0.003193
F-statistic: 1.183 on 4 and 225 DF, p-value: 0.3189
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.078514
Standard error............: 0.124301
Odds ratio (effect size)..: 1.082
Lower 95% CI..............: 0.848
Upper 95% CI..............: 1.38
T-value...................: 0.631646
P-value...................: 0.5282593
R^2.......................: 0.020605
Adjusted r^2..............: 0.003193
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing VEGFA_rankfilter: removed 421 rows (68%), 201 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
1.114
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.4236 -1.1022 -0.2840 0.1626 8.8644
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.1921938 4.5135792 1.150 0.251
currentDF[, TRAIT] -0.0208112 0.1335547 -0.156 0.876
Age -0.0067017 0.0148019 -0.453 0.651
Gendermale 0.1381205 0.2761542 0.500 0.618
ORdate_epoch -0.0002966 0.0003547 -0.836 0.404
Residual standard error: 1.696 on 196 degrees of freedom
Multiple R-squared: 0.008483, Adjusted R-squared: -0.01175
F-statistic: 0.4192 on 4 and 196 DF, p-value: 0.7946
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: VEGFA_rank
Effect size...............: -0.020811
Standard error............: 0.133555
Odds ratio (effect size)..: 0.979
Lower 95% CI..............: 0.754
Upper 95% CI..............: 1.272
T-value...................: -0.155825
P-value...................: 0.876331
R^2.......................: 0.008483
Adjusted r^2..............: -0.011752
Sample size of AE DB......: 622
Sample size of model......: 201
Missing data %............: 67.68489
- processing TGFB_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
6.7301681 -0.0004431
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.6888 -1.1462 -0.3846 0.4296 11.2378
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.0545748 4.0688009 1.980 0.049 *
currentDF[, TRAIT] -0.0552007 0.1221205 -0.452 0.652
Age -0.0186150 0.0147203 -1.265 0.207
Gendermale 0.0317045 0.2741846 0.116 0.908
ORdate_epoch -0.0004501 0.0003142 -1.432 0.153
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.813 on 226 degrees of freedom
Multiple R-squared: 0.0166, Adjusted R-squared: -0.0008069
F-statistic: 0.9536 on 4 and 226 DF, p-value: 0.4338
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: TGFB_rank
Effect size...............: -0.055201
Standard error............: 0.12212
Odds ratio (effect size)..: 0.946
Lower 95% CI..............: 0.745
Upper 95% CI..............: 1.202
T-value...................: -0.452018
P-value...................: 0.6516892
R^2.......................: 0.016598
Adjusted r^2..............: -0.000807
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP2_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
8.1164990 -0.0005594
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.7167 -1.1221 -0.4249 0.3331 11.1493
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.4867994 4.0618157 2.336 0.0204 *
currentDF[, TRAIT] -0.0850339 0.1207955 -0.704 0.4822
Age -0.0193699 0.0143887 -1.346 0.1796
Gendermale 0.1244951 0.2719640 0.458 0.6476
ORdate_epoch -0.0005712 0.0003162 -1.806 0.0722 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.78 on 226 degrees of freedom
Multiple R-squared: 0.02475, Adjusted R-squared: 0.007486
F-statistic: 1.434 on 4 and 226 DF, p-value: 0.2237
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MMP2_rank
Effect size...............: -0.085034
Standard error............: 0.120795
Odds ratio (effect size)..: 0.918
Lower 95% CI..............: 0.725
Upper 95% CI..............: 1.164
T-value...................: -0.703949
P-value...................: 0.4821893
R^2.......................: 0.024747
Adjusted r^2..............: 0.007486
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP8_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
8.1164990 -0.0005594
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.7527 -1.0843 -0.4417 0.3890 11.3520
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.0725255 3.9903143 2.274 0.0239 *
currentDF[, TRAIT] 0.1261784 0.1180766 1.069 0.2864
Age -0.0181829 0.0143133 -1.270 0.2053
Gendermale 0.1218940 0.2699790 0.451 0.6521
ORdate_epoch -0.0005445 0.0003120 -1.745 0.0824 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.778 on 226 degrees of freedom
Multiple R-squared: 0.02752, Adjusted R-squared: 0.01031
F-statistic: 1.599 on 4 and 226 DF, p-value: 0.1755
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MMP8_rank
Effect size...............: 0.126178
Standard error............: 0.118077
Odds ratio (effect size)..: 1.134
Lower 95% CI..............: 0.9
Upper 95% CI..............: 1.43
T-value...................: 1.068614
P-value...................: 0.2863835
R^2.......................: 0.027523
Adjusted r^2..............: 0.010311
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP9_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
8.1164990 -0.0005594
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.8686 -1.0633 -0.4639 0.4149 11.4019
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.3843517 4.0080256 2.092 0.0376 *
currentDF[, TRAIT] 0.1564731 0.1178386 1.328 0.1856
Age -0.0181903 0.0142928 -1.273 0.2044
Gendermale 0.1564497 0.2679138 0.584 0.5598
ORdate_epoch -0.0004915 0.0003134 -1.568 0.1182
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.775 on 226 degrees of freedom
Multiple R-squared: 0.03018, Adjusted R-squared: 0.01301
F-statistic: 1.758 on 4 and 226 DF, p-value: 0.1383
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MMP9_rank
Effect size...............: 0.156473
Standard error............: 0.117839
Odds ratio (effect size)..: 1.169
Lower 95% CI..............: 0.928
Upper 95% CI..............: 1.473
T-value...................: 1.32786
P-value...................: 0.1855638
R^2.......................: 0.030175
Adjusted r^2..............: 0.01301
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
Analysis of COL4A1.
- processing IL2_rankfilter: removed 440 rows (71%), 182 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
188.3
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-213.13 -117.70 -66.53 37.24 1541.83
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -317.05668 628.04850 -0.505 0.614
currentDF[, TRAIT] -14.45029 17.42979 -0.829 0.408
Age -0.53457 2.10405 -0.254 0.800
Gendermale 37.16274 40.81691 0.910 0.364
ORdate_epoch 0.04124 0.04968 0.830 0.408
Residual standard error: 230.3 on 177 degrees of freedom
Multiple R-squared: 0.0158, Adjusted R-squared: -0.006447
F-statistic: 0.7101 on 4 and 177 DF, p-value: 0.586
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL2_rank
Effect size...............: -14.45029
Standard error............: 17.42979
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 363790224
T-value...................: -0.829057
P-value...................: 0.4081886
R^2.......................: 0.015795
Adjusted r^2..............: -0.006447
Sample size of AE DB......: 622
Sample size of model......: 182
Missing data %............: 70.73955
- processing IL4_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
200.8
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-230.56 -132.85 -73.12 25.93 2099.95
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -767.53602 868.35914 -0.884 0.378
currentDF[, TRAIT] -13.38814 23.51100 -0.569 0.570
Age -0.02810 2.81398 -0.010 0.992
Gendermale 56.31212 55.19838 1.020 0.309
ORdate_epoch 0.07475 0.06892 1.085 0.280
Residual standard error: 291.4 on 159 degrees of freedom
Multiple R-squared: 0.02034, Adjusted R-squared: -0.004308
F-statistic: 0.8252 on 4 and 159 DF, p-value: 0.5109
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL4_rank
Effect size...............: -13.38814
Standard error............: 23.511
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 1.579681e+14
T-value...................: -0.569442
P-value...................: 0.56986
R^2.......................: 0.020337
Adjusted r^2..............: -0.004308
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing IL5_rankfilter: removed 443 rows (71%), 179 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
198.66 -34.34
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-244.51 -129.50 -73.56 26.50 2117.59
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -654.85855 789.28468 -0.830 0.408
currentDF[, TRAIT] -28.78463 21.53674 -1.337 0.183
Age -0.30554 2.60248 -0.117 0.907
Gendermale 48.37542 50.57379 0.957 0.340
ORdate_epoch 0.06741 0.06326 1.066 0.288
Residual standard error: 281 on 174 degrees of freedom
Multiple R-squared: 0.02754, Adjusted R-squared: 0.005185
F-statistic: 1.232 on 4 and 174 DF, p-value: 0.2991
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL5_rank
Effect size...............: -28.78463
Standard error............: 21.53674
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 678325.2
T-value...................: -1.336536
P-value...................: 0.1831192
R^2.......................: 0.027541
Adjusted r^2..............: 0.005185
Sample size of AE DB......: 622
Sample size of model......: 179
Missing data %............: 71.22186
- processing IL6_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
195.2
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-229.29 -122.84 -76.57 15.18 2132.20
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -621.70357 690.10018 -0.901 0.369
currentDF[, TRAIT] -17.98881 20.58333 -0.874 0.383
Age 0.47329 2.49217 0.190 0.850
Gendermale 46.74868 46.44932 1.006 0.316
ORdate_epoch 0.06019 0.05508 1.093 0.276
Residual standard error: 275.3 on 184 degrees of freedom
Multiple R-squared: 0.01806, Adjusted R-squared: -0.003285
F-statistic: 0.8461 on 4 and 184 DF, p-value: 0.4976
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL6_rank
Effect size...............: -17.98881
Standard error............: 20.58333
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 5110250474
T-value...................: -0.873951
P-value...................: 0.3832844
R^2.......................: 0.018061
Adjusted r^2..............: -0.003285
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing IL8_rankfilter: removed 442 rows (71%), 180 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
187.8
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-215.30 -113.97 -55.21 29.32 2141.67
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -487.70975 650.10915 -0.750 0.454
currentDF[, TRAIT] 12.21431 19.40409 0.629 0.530
Age -0.40496 2.22033 -0.182 0.855
Gendermale 27.28702 44.18316 0.618 0.538
ORdate_epoch 0.05467 0.05168 1.058 0.292
Residual standard error: 248.8 on 175 degrees of freedom
Multiple R-squared: 0.01434, Adjusted R-squared: -0.008187
F-statistic: 0.6366 on 4 and 175 DF, p-value: 0.6371
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL8_rank
Effect size...............: 12.21431
Standard error............: 19.40409
Odds ratio (effect size)..: 201654.2
Lower 95% CI..............: 0
Upper 95% CI..............: 6.632824e+21
T-value...................: 0.629471
P-value...................: 0.5298617
R^2.......................: 0.014342
Adjusted r^2..............: -0.008187
Sample size of AE DB......: 622
Sample size of model......: 180
Missing data %............: 71.06109
- processing IL9_rankfilter: removed 412 rows (66%), 210 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
205.20 39.72
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-335.71 -127.48 -68.12 22.30 2019.38
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -792.08915 651.20572 -1.216 0.2253
currentDF[, TRAIT] 41.31384 20.02806 2.063 0.0404 *
Age 0.88111 2.47091 0.357 0.7218
Gendermale 58.72943 46.04252 1.276 0.2036
ORdate_epoch 0.07108 0.04986 1.426 0.1555
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 287.4 on 205 degrees of freedom
Multiple R-squared: 0.03564, Adjusted R-squared: 0.01682
F-statistic: 1.894 on 4 and 205 DF, p-value: 0.1128
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL9_rank
Effect size...............: 41.31384
Standard error............: 20.02806
Odds ratio (effect size)..: 8.757322e+17
Lower 95% CI..............: 7.837
Upper 95% CI..............: 9.785901e+34
T-value...................: 2.062798
P-value...................: 0.04039223
R^2.......................: 0.035639
Adjusted r^2..............: 0.016823
Sample size of AE DB......: 622
Sample size of model......: 210
Missing data %............: 66.23794
- processing IL10_rankfilter: removed 465 rows (75%), 157 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
186.2
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-204.85 -122.25 -68.75 23.17 1546.21
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -393.48539 769.49394 -0.511 0.610
currentDF[, TRAIT] -9.14838 20.26396 -0.451 0.652
Age -0.45515 2.41117 -0.189 0.851
Gendermale 42.34724 46.72262 0.906 0.366
ORdate_epoch 0.04666 0.06040 0.773 0.441
Residual standard error: 243.5 on 152 degrees of freedom
Multiple R-squared: 0.01375, Adjusted R-squared: -0.0122
F-statistic: 0.5298 on 4 and 152 DF, p-value: 0.714
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL10_rank
Effect size...............: -9.148382
Standard error............: 20.26396
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 1.887714e+13
T-value...................: -0.451461
P-value...................: 0.652301
R^2.......................: 0.013751
Adjusted r^2..............: -0.012203
Sample size of AE DB......: 622
Sample size of model......: 157
Missing data %............: 74.75884
- processing IL12_rankfilter: removed 456 rows (73%), 166 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
205.81 -36.71
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-249.55 -137.32 -76.19 40.86 2077.24
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -554.87789 867.45181 -0.640 0.523
currentDF[, TRAIT] -31.43446 23.17946 -1.356 0.177
Age -0.60123 2.80845 -0.214 0.831
Gendermale 55.96986 54.09862 1.035 0.302
ORdate_epoch 0.06109 0.06867 0.890 0.375
Residual standard error: 290.1 on 161 degrees of freedom
Multiple R-squared: 0.02882, Adjusted R-squared: 0.004693
F-statistic: 1.194 on 4 and 161 DF, p-value: 0.3153
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL12_rank
Effect size...............: -31.43446
Standard error............: 23.17946
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 1199330
T-value...................: -1.356135
P-value...................: 0.1769553
R^2.......................: 0.028822
Adjusted r^2..............: 0.004693
Sample size of AE DB......: 622
Sample size of model......: 166
Missing data %............: 73.3119
- processing IL13_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
205.64 34.02
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-303.18 -124.44 -76.26 9.90 2045.44
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -725.83440 635.95306 -1.141 0.2549
currentDF[, TRAIT] 32.60273 19.10689 1.706 0.0893 .
Age 1.67169 2.32723 0.718 0.4733
Gendermale 43.17562 43.76688 0.986 0.3250
ORdate_epoch 0.06255 0.04909 1.274 0.2039
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 285.5 on 225 degrees of freedom
Multiple R-squared: 0.02704, Adjusted R-squared: 0.009739
F-statistic: 1.563 on 4 and 225 DF, p-value: 0.1851
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL13_rank
Effect size...............: 32.60273
Standard error............: 19.10689
Odds ratio (effect size)..: 1.44273e+14
Lower 95% CI..............: 0.008
Upper 95% CI..............: 2.650307e+30
T-value...................: 1.706334
P-value...................: 0.08932573
R^2.......................: 0.027036
Adjusted r^2..............: 0.009739
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing IL21_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
205.64 27.33
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-275.74 -127.95 -78.99 9.43 2062.49
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -752.64578 637.00504 -1.182 0.239
currentDF[, TRAIT] 25.65886 19.08899 1.344 0.180
Age 1.48949 2.32637 0.640 0.523
Gendermale 43.34273 43.91957 0.987 0.325
ORdate_epoch 0.06566 0.04912 1.337 0.183
Residual standard error: 286.2 on 225 degrees of freedom
Multiple R-squared: 0.0223, Adjusted R-squared: 0.004915
F-statistic: 1.283 on 4 and 225 DF, p-value: 0.2776
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL21_rank
Effect size...............: 25.65886
Standard error............: 19.08899
Odds ratio (effect size)..: 139155894574
Lower 95% CI..............: 0
Upper 95% CI..............: 2.468187e+27
T-value...................: 1.344171
P-value...................: 0.1802469
R^2.......................: 0.022297
Adjusted r^2..............: 0.004915
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing INFG_rankfilter: removed 449 rows (72%), 173 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
204.3
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-226.73 -131.07 -72.60 25.29 2110.51
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -685.78217 818.59690 -0.838 0.403
currentDF[, TRAIT] 1.24188 23.61012 0.053 0.958
Age 0.31646 2.70700 0.117 0.907
Gendermale 53.81032 53.92417 0.998 0.320
ORdate_epoch 0.06647 0.06440 1.032 0.303
Residual standard error: 286.4 on 168 degrees of freedom
Multiple R-squared: 0.01462, Adjusted R-squared: -0.008838
F-statistic: 0.6233 on 4 and 168 DF, p-value: 0.6465
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: INFG_rank
Effect size...............: 1.241881
Standard error............: 23.61012
Odds ratio (effect size)..: 3.462
Lower 95% CI..............: 0
Upper 95% CI..............: 4.331971e+20
T-value...................: 0.0526
P-value...................: 0.9581135
R^2.......................: 0.014623
Adjusted r^2..............: -0.008838
Sample size of AE DB......: 622
Sample size of model......: 173
Missing data %............: 72.18649
- processing TNFA_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
201.43 -37.62
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-237.45 -135.63 -80.97 49.63 2069.96
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -484.54955 861.51554 -0.562 0.575
currentDF[, TRAIT] -33.72112 23.04675 -1.463 0.145
Age 0.02559 2.81822 0.009 0.993
Gendermale 50.23601 53.49291 0.939 0.349
ORdate_epoch 0.05203 0.06793 0.766 0.445
Residual standard error: 290.4 on 159 degrees of freedom
Multiple R-squared: 0.0265, Adjusted R-squared: 0.002008
F-statistic: 1.082 on 4 and 159 DF, p-value: 0.3673
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: TNFA_rank
Effect size...............: -33.72112
Standard error............: 23.04675
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 93948.92
T-value...................: -1.463162
P-value...................: 0.1453971
R^2.......................: 0.026498
Adjusted r^2..............: 0.002008
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing MIF_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
205.6
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-223.16 -127.42 -77.97 12.67 2127.04
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -930.58713 714.92666 -1.302 0.194
currentDF[, TRAIT] 9.63732 21.31285 0.452 0.652
Age 1.25346 2.32744 0.539 0.591
Gendermale 48.38055 43.94155 1.101 0.272
ORdate_epoch 0.08080 0.05517 1.465 0.144
Residual standard error: 287.2 on 225 degrees of freedom
Multiple R-squared: 0.01534, Adjusted R-squared: -0.002165
F-statistic: 0.8763 on 4 and 225 DF, p-value: 0.4788
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MIF_rank
Effect size...............: 9.637319
Standard error............: 21.31285
Odds ratio (effect size)..: 15326.19
Lower 95% CI..............: 0
Upper 95% CI..............: 2.124704e+22
T-value...................: 0.452183
P-value...................: 0.6515724
R^2.......................: 0.01534
Adjusted r^2..............: -0.002165
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing MCP1_rankfilter: removed 394 rows (63%), 228 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
206.8
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-247.31 -129.28 -74.16 6.13 2108.99
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -901.73286 655.69621 -1.375 0.170
currentDF[, TRAIT] 17.31190 19.62992 0.882 0.379
Age 1.61165 2.35908 0.683 0.495
Gendermale 46.58601 44.42933 1.049 0.296
ORdate_epoch 0.07676 0.05039 1.523 0.129
Residual standard error: 287.8 on 223 degrees of freedom
Multiple R-squared: 0.01796, Adjusted R-squared: 0.0003434
F-statistic: 1.019 on 4 and 223 DF, p-value: 0.3981
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MCP1_rank
Effect size...............: 17.3119
Standard error............: 19.62992
Odds ratio (effect size)..: 32996257
Lower 95% CI..............: 0
Upper 95% CI..............: 1.689609e+24
T-value...................: 0.881914
P-value...................: 0.3787727
R^2.......................: 0.017958
Adjusted r^2..............: 0.000343
Sample size of AE DB......: 622
Sample size of model......: 228
Missing data %............: 63.34405
- processing MIP1a_rankfilter: removed 408 rows (66%), 214 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
205.7
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-273.08 -123.20 -75.68 14.18 2066.46
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -706.69839 649.55398 -1.088 0.278
currentDF[, TRAIT] 22.75281 19.84147 1.147 0.253
Age 0.86772 2.41348 0.360 0.720
Gendermale 58.42077 46.17518 1.265 0.207
ORdate_epoch 0.06442 0.04991 1.291 0.198
Residual standard error: 288.5 on 209 degrees of freedom
Multiple R-squared: 0.02217, Adjusted R-squared: 0.00346
F-statistic: 1.185 on 4 and 209 DF, p-value: 0.3185
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MIP1a_rank
Effect size...............: 22.75281
Standard error............: 19.84147
Odds ratio (effect size)..: 7610585913
Lower 95% CI..............: 0
Upper 95% CI..............: 5.8995e+26
T-value...................: 1.14673
P-value...................: 0.2528043
R^2.......................: 0.022175
Adjusted r^2..............: 0.00346
Sample size of AE DB......: 622
Sample size of model......: 214
Missing data %............: 65.59485
- processing RANTES_rankfilter: removed 396 rows (64%), 226 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-1081.0194 35.0248 0.1025
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-266.70 -129.52 -75.71 27.56 2068.67
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.343e+03 7.143e+02 -1.881 0.0613 .
currentDF[, TRAIT] 3.860e+01 2.146e+01 1.799 0.0734 .
Age 2.086e+00 2.382e+00 0.876 0.3821
Gendermale 4.866e+01 4.469e+01 1.089 0.2775
ORdate_epoch 1.092e-01 5.423e-02 2.014 0.0452 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 287.6 on 221 degrees of freedom
Multiple R-squared: 0.02859, Adjusted R-squared: 0.01101
F-statistic: 1.626 on 4 and 221 DF, p-value: 0.1687
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: RANTES_rank
Effect size...............: 38.5955
Standard error............: 21.45887
Odds ratio (effect size)..: 5.778455e+16
Lower 95% CI..............: 0.031
Upper 95% CI..............: 1.066521e+35
T-value...................: 1.79858
P-value...................: 0.07344962
R^2.......................: 0.028589
Adjusted r^2..............: 0.011007
Sample size of AE DB......: 622
Sample size of model......: 226
Missing data %............: 63.6656
- processing MIG_rankfilter: removed 395 rows (64%), 227 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
205.78 41.42
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-310.04 -127.38 -67.83 14.93 2040.06
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -472.50241 661.04325 -0.715 0.475
currentDF[, TRAIT] 38.19963 20.28410 1.883 0.061 .
Age 1.95581 2.35601 0.830 0.407
Gendermale 42.18063 44.40233 0.950 0.343
ORdate_epoch 0.04091 0.05157 0.793 0.428
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 286.8 on 222 degrees of freedom
Multiple R-squared: 0.03013, Adjusted R-squared: 0.01266
F-statistic: 1.724 on 4 and 222 DF, p-value: 0.1456
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MIG_rank
Effect size...............: 38.19963
Standard error............: 20.2841
Odds ratio (effect size)..: 3.88945e+16
Lower 95% CI..............: 0.211
Upper 95% CI..............: 7.178969e+33
T-value...................: 1.88323
P-value...................: 0.06097663
R^2.......................: 0.030131
Adjusted r^2..............: 0.012656
Sample size of AE DB......: 622
Sample size of model......: 227
Missing data %............: 63.50482
- processing IP10_rankfilter: removed 415 rows (67%), 207 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_epoch
-755.60678 47.74536 70.08152 0.07247
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-344.89 -129.23 -69.29 18.14 1964.10
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -854.87340 664.82462 -1.286 0.2000
currentDF[, TRAIT] 49.82612 20.47094 2.434 0.0158 *
Age 1.46653 2.49915 0.587 0.5580
Gendermale 70.24809 45.63088 1.539 0.1253
ORdate_epoch 0.07246 0.05091 1.423 0.1562
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 289.2 on 202 degrees of freedom
Multiple R-squared: 0.04767, Adjusted R-squared: 0.02881
F-statistic: 2.528 on 4 and 202 DF, p-value: 0.04189
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IP10_rank
Effect size...............: 49.82612
Standard error............: 20.47094
Odds ratio (effect size)..: 4.357218e+21
Lower 95% CI..............: 16368.06
Upper 95% CI..............: 1.159903e+39
T-value...................: 2.433993
P-value...................: 0.01580257
R^2.......................: 0.047669
Adjusted r^2..............: 0.028811
Sample size of AE DB......: 622
Sample size of model......: 207
Missing data %............: 66.72026
- processing Eotaxin1_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
205.6
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-245.04 -127.92 -77.39 7.17 2095.01
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -722.70239 645.19678 -1.120 0.264
currentDF[, TRAIT] 13.01896 19.32326 0.674 0.501
Age 1.29994 2.32795 0.558 0.577
Gendermale 45.29038 44.10129 1.027 0.306
ORdate_epoch 0.06418 0.04982 1.288 0.199
Residual standard error: 287 on 225 degrees of freedom
Multiple R-squared: 0.01643, Adjusted R-squared: -0.001056
F-statistic: 0.9396 on 4 and 225 DF, p-value: 0.4418
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 13.01896
Standard error............: 19.32326
Odds ratio (effect size)..: 450883.7
Lower 95% CI..............: 0
Upper 95% CI..............: 1.265773e+22
T-value...................: 0.673746
P-value...................: 0.5011645
R^2.......................: 0.01643
Adjusted r^2..............: -0.001056
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing TARC_rankfilter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_epoch
-1.047e+03 4.269e+01 7.399e+01 9.568e-02
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-330.95 -142.36 -73.90 17.26 1988.11
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.324e+03 8.482e+02 -1.561 0.1202
currentDF[, TRAIT] 4.666e+01 2.281e+01 2.046 0.0421 *
Age 2.891e+00 2.616e+00 1.105 0.2705
Gendermale 7.736e+01 4.914e+01 1.574 0.1170
ORdate_epoch 1.019e-01 6.407e-02 1.590 0.1134
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 299.1 on 198 degrees of freedom
Multiple R-squared: 0.03543, Adjusted R-squared: 0.01594
F-statistic: 1.818 on 4 and 198 DF, p-value: 0.1268
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: TARC_rank
Effect size...............: 46.65927
Standard error............: 22.81021
Odds ratio (effect size)..: 1.835967e+20
Lower 95% CI..............: 7.037
Upper 95% CI..............: 4.789734e+39
T-value...................: 2.045543
P-value...................: 0.04212381
R^2.......................: 0.035428
Adjusted r^2..............: 0.015942
Sample size of AE DB......: 622
Sample size of model......: 203
Missing data %............: 67.36334
- processing PARC_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-1.010e+03 3.445e+01 9.675e-02
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-250.57 -133.27 -67.80 23.21 2055.24
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.222e+03 6.765e+02 -1.806 0.0722 .
currentDF[, TRAIT] 3.735e+01 2.013e+01 1.855 0.0649 .
Age 1.490e+00 2.314e+00 0.644 0.5202
Gendermale 5.475e+01 4.377e+01 1.251 0.2123
ORdate_epoch 1.023e-01 5.197e-02 1.969 0.0501 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 285.1 on 225 degrees of freedom
Multiple R-squared: 0.02929, Adjusted R-squared: 0.01204
F-statistic: 1.697 on 4 and 225 DF, p-value: 0.1515
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: PARC_rank
Effect size...............: 37.34501
Standard error............: 20.13069
Odds ratio (effect size)..: 1.654753e+16
Lower 95% CI..............: 0.121
Upper 95% CI..............: 2.261118e+33
T-value...................: 1.855128
P-value...................: 0.06488597
R^2.......................: 0.029293
Adjusted r^2..............: 0.012036
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing MDC_rankfilter: removed 407 rows (65%), 215 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
207.6
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-297.43 -130.77 -82.59 25.49 2047.83
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.074e+03 6.853e+02 -1.568 0.1184
currentDF[, TRAIT] 2.957e+01 2.085e+01 1.418 0.1576
Age 1.171e+00 2.423e+00 0.483 0.6295
Gendermale 6.975e+01 4.580e+01 1.523 0.1293
ORdate_epoch 9.153e-02 5.251e-02 1.743 0.0828 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 288.8 on 210 degrees of freedom
Multiple R-squared: 0.02775, Adjusted R-squared: 0.009231
F-statistic: 1.498 on 4 and 210 DF, p-value: 0.2038
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MDC_rank
Effect size...............: 29.57013
Standard error............: 20.85122
Odds ratio (effect size)..: 6.952573e+12
Lower 95% CI..............: 0
Upper 95% CI..............: 3.900005e+30
T-value...................: 1.418149
P-value...................: 0.1576296
R^2.......................: 0.02775
Adjusted r^2..............: 0.009231
Sample size of AE DB......: 622
Sample size of model......: 215
Missing data %............: 65.43408
- processing OPG_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
205.6 27.6
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-290.59 -130.79 -76.44 15.85 2055.80
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -777.10271 636.20688 -1.221 0.223
currentDF[, TRAIT] 27.39706 19.07990 1.436 0.152
Age 1.60325 2.33198 0.688 0.492
Gendermale 45.04050 43.80538 1.028 0.305
ORdate_epoch 0.06689 0.04905 1.364 0.174
Residual standard error: 286 on 225 degrees of freedom
Multiple R-squared: 0.02339, Adjusted R-squared: 0.006033
F-statistic: 1.347 on 4 and 225 DF, p-value: 0.2532
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: OPG_rank
Effect size...............: 27.39706
Standard error............: 19.0799
Odds ratio (effect size)..: 791389492757
Lower 95% CI..............: 0
Upper 95% CI..............: 1.378881e+28
T-value...................: 1.435912
P-value...................: 0.1524162
R^2.......................: 0.023395
Adjusted r^2..............: 0.006033
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing sICAM1_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
205.6
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-214.09 -129.11 -73.54 10.71 2113.04
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -781.10158 659.56071 -1.184 0.238
currentDF[, TRAIT] -0.52191 19.68239 -0.027 0.979
Age 1.19552 2.35059 0.509 0.612
Gendermale 47.99473 43.99985 1.091 0.277
ORdate_epoch 0.06923 0.05046 1.372 0.171
Residual standard error: 287.3 on 225 degrees of freedom
Multiple R-squared: 0.01445, Adjusted R-squared: -0.003072
F-statistic: 0.8246 on 4 and 225 DF, p-value: 0.5107
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: sICAM1_rank
Effect size...............: -0.521912
Standard error............: 19.68239
Odds ratio (effect size)..: 0.593
Lower 95% CI..............: 0
Upper 95% CI..............: 3.367608e+16
T-value...................: -0.026517
P-value...................: 0.9788687
R^2.......................: 0.014449
Adjusted r^2..............: -0.003072
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing VEGFA_rankfilter: removed 421 rows (68%), 201 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
204.7
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-210.07 -129.65 -71.82 4.26 2112.57
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -696.91443 741.53124 -0.940 0.348
currentDF[, TRAIT] -1.33360 21.94157 -0.061 0.952
Age 1.73942 2.43179 0.715 0.475
Gendermale 48.67837 45.36909 1.073 0.285
ORdate_epoch 0.05952 0.05828 1.021 0.308
Residual standard error: 278.6 on 196 degrees of freedom
Multiple R-squared: 0.01309, Adjusted R-squared: -0.007054
F-statistic: 0.6498 on 4 and 196 DF, p-value: 0.6277
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: VEGFA_rank
Effect size...............: -1.333605
Standard error............: 21.94157
Odds ratio (effect size)..: 0.264
Lower 95% CI..............: 0
Upper 95% CI..............: 1.252742e+18
T-value...................: -0.06078
P-value...................: 0.9515965
R^2.......................: 0.013087
Adjusted r^2..............: -0.007054
Sample size of AE DB......: 622
Sample size of model......: 201
Missing data %............: 67.68489
- processing TGFB_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-667.15046 0.06965
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-221.83 -132.17 -75.20 19.34 2116.15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -847.45948 643.54080 -1.317 0.189
currentDF[, TRAIT] 4.97623 19.31515 0.258 0.797
Age 1.53386 2.32823 0.659 0.511
Gendermale 42.23258 43.36633 0.974 0.331
ORdate_epoch 0.07325 0.04970 1.474 0.142
Residual standard error: 286.8 on 226 degrees of freedom
Multiple R-squared: 0.01491, Adjusted R-squared: -0.002528
F-statistic: 0.855 on 4 and 226 DF, p-value: 0.4918
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: TGFB_rank
Effect size...............: 4.976229
Standard error............: 19.31515
Odds ratio (effect size)..: 144.927
Lower 95% CI..............: 0
Upper 95% CI..............: 4.004408e+18
T-value...................: 0.257633
P-value...................: 0.7969243
R^2.......................: 0.014907
Adjusted r^2..............: -0.002528
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP2_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
197.3
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-207.50 -121.48 -67.92 15.73 2128.45
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -422.58909 600.71406 -0.703 0.482
currentDF[, TRAIT] -8.59638 17.86481 -0.481 0.631
Age 1.08386 2.12798 0.509 0.611
Gendermale 35.16087 40.22157 0.874 0.383
ORdate_epoch 0.04152 0.04677 0.888 0.376
Residual standard error: 263.3 on 226 degrees of freedom
Multiple R-squared: 0.0106, Adjusted R-squared: -0.006908
F-statistic: 0.6055 on 4 and 226 DF, p-value: 0.6591
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MMP2_rank
Effect size...............: -8.596383
Standard error............: 17.86481
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 297487232388
T-value...................: -0.481191
P-value...................: 0.6308464
R^2.......................: 0.010603
Adjusted r^2..............: -0.006908
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP8_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
197.3
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-223.61 -120.49 -75.25 15.77 2133.39
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -459.17609 589.69775 -0.779 0.437
currentDF[, TRAIT] 19.25183 17.44964 1.103 0.271
Age 1.21842 2.11525 0.576 0.565
Gendermale 33.23172 39.89811 0.833 0.406
ORdate_epoch 0.04382 0.04611 0.950 0.343
Residual standard error: 262.7 on 226 degrees of freedom
Multiple R-squared: 0.0149, Adjusted R-squared: -0.00254
F-statistic: 0.8543 on 4 and 226 DF, p-value: 0.4922
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MMP8_rank
Effect size...............: 19.25183
Standard error............: 17.44964
Odds ratio (effect size)..: 229594660
Lower 95% CI..............: 0
Upper 95% CI..............: 1.638295e+23
T-value...................: 1.103279
P-value...................: 0.2710792
R^2.......................: 0.014895
Adjusted r^2..............: -0.00254
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP9_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
197.27 28.48
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-228.73 -122.63 -64.32 34.22 2123.20
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -590.74040 590.57612 -1.000 0.3182
currentDF[, TRAIT] 30.97537 17.36332 1.784 0.0758 .
Age 1.22980 2.10602 0.584 0.5598
Gendermale 38.60355 39.47666 0.978 0.3292
ORdate_epoch 0.05394 0.04618 1.168 0.2440
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 261.6 on 226 degrees of freedom
Multiple R-squared: 0.02334, Adjusted R-squared: 0.006057
F-statistic: 1.35 on 4 and 226 DF, p-value: 0.2522
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MMP9_rank
Effect size...............: 30.97537
Standard error............: 17.36332
Odds ratio (effect size)..: 2.834218e+13
Lower 95% CI..............: 0.047
Upper 95% CI..............: 1.707617e+28
T-value...................: 1.783954
P-value...................: 0.07577275
R^2.......................: 0.023343
Adjusted r^2..............: 0.006057
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
Analysis of COL4A2.
- processing IL2_rankfilter: removed 440 rows (71%), 182 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
368.1
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-461.0 -291.5 -186.1 -5.6 7540.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1945.1003 2089.6614 -0.931 0.353
currentDF[, TRAIT] -19.3127 57.9929 -0.333 0.740
Age -0.1441 7.0007 -0.021 0.984
Gendermale 122.2846 135.8072 0.900 0.369
ORdate_epoch 0.1792 0.1653 1.084 0.280
Residual standard error: 766.3 on 177 degrees of freedom
Multiple R-squared: 0.01418, Adjusted R-squared: -0.008097
F-statistic: 0.6366 on 4 and 177 DF, p-value: 0.6371
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL2_rank
Effect size...............: -19.31268
Standard error............: 57.9929
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 9.487646e+40
T-value...................: -0.333018
P-value...................: 0.7395148
R^2.......................: 0.014181
Adjusted r^2..............: -0.008097
Sample size of AE DB......: 622
Sample size of model......: 182
Missing data %............: 70.73955
- processing IL4_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-3859.8346 0.3449
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-609.2 -347.3 -229.4 -19.8 7860.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3571.8245 3001.7681 -1.190 0.236
currentDF[, TRAIT] -30.9619 81.2735 -0.381 0.704
Age 1.4386 9.7275 0.148 0.883
Gendermale 198.1260 190.8113 1.038 0.301
ORdate_epoch 0.3016 0.2382 1.266 0.207
Residual standard error: 1007 on 159 degrees of freedom
Multiple R-squared: 0.02227, Adjusted R-squared: -0.002331
F-statistic: 0.9052 on 4 and 159 DF, p-value: 0.4624
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL4_rank
Effect size...............: -30.96185
Standard error............: 81.27347
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 5.430201e+55
T-value...................: -0.380959
P-value...................: 0.7037423
R^2.......................: 0.022266
Adjusted r^2..............: -0.002331
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing IL5_rankfilter: removed 443 rows (71%), 179 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-3551.8636 0.3195
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-643.0 -334.1 -224.5 4.6 7906.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3119.5274 2741.8148 -1.138 0.257
currentDF[, TRAIT] -59.2424 74.8143 -0.792 0.430
Age 0.5915 9.0405 0.065 0.948
Gendermale 167.0915 175.6831 0.951 0.343
ORdate_epoch 0.2712 0.2198 1.234 0.219
Residual standard error: 976.1 on 174 degrees of freedom
Multiple R-squared: 0.02242, Adjusted R-squared: -4.937e-05
F-statistic: 0.9978 on 4 and 174 DF, p-value: 0.4103
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL5_rank
Effect size...............: -59.24243
Standard error............: 74.81427
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 9.006101e+37
T-value...................: -0.79186
P-value...................: 0.4295205
R^2.......................: 0.022424
Adjusted r^2..............: -4.9e-05
Sample size of AE DB......: 622
Sample size of model......: 179
Missing data %............: 71.22186
- processing IL6_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
403.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-518.8 -329.3 -227.5 -45.9 7934.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2253.2474 2379.9327 -0.947 0.345
currentDF[, TRAIT] -6.3536 70.9853 -0.090 0.929
Age 2.4920 8.5947 0.290 0.772
Gendermale 166.1146 160.1887 1.037 0.301
ORdate_epoch 0.1897 0.1899 0.999 0.319
Residual standard error: 949.4 on 184 degrees of freedom
Multiple R-squared: 0.01304, Adjusted R-squared: -0.008413
F-statistic: 0.6079 on 4 and 184 DF, p-value: 0.6575
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL6_rank
Effect size...............: -6.353606
Standard error............: 70.98526
Odds ratio (effect size)..: 0.002
Lower 95% CI..............: 0
Upper 95% CI..............: 4.61887e+57
T-value...................: -0.089506
P-value...................: 0.9287771
R^2.......................: 0.013042
Adjusted r^2..............: -0.008413
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing IL8_rankfilter: removed 442 rows (71%), 180 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-2486.1323 0.2281
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-508.4 -286.0 -163.6 6.5 8013.7
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2474.2315 2020.5508 -1.225 0.222
currentDF[, TRAIT] -19.0319 60.3082 -0.316 0.753
Age -4.0754 6.9008 -0.591 0.556
Gendermale 101.8304 137.3221 0.742 0.459
ORdate_epoch 0.2430 0.1606 1.513 0.132
Residual standard error: 773.2 on 175 degrees of freedom
Multiple R-squared: 0.01725, Adjusted R-squared: -0.005212
F-statistic: 0.768 on 4 and 175 DF, p-value: 0.5474
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL8_rank
Effect size...............: -19.03186
Standard error............: 60.30824
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 1.174851e+43
T-value...................: -0.315576
P-value...................: 0.7527001
R^2.......................: 0.017251
Adjusted r^2..............: -0.005212
Sample size of AE DB......: 622
Sample size of model......: 180
Missing data %............: 71.06109
- processing IL9_rankfilter: removed 412 rows (66%), 210 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
425.6
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-770.8 -350.9 -216.3 -14.5 7718.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3192.8936 2252.4646 -1.418 0.158
currentDF[, TRAIT] 85.8878 69.2753 1.240 0.216
Age 4.6972 8.5467 0.550 0.583
Gendermale 189.2008 159.2571 1.188 0.236
ORdate_epoch 0.2513 0.1725 1.457 0.147
Residual standard error: 994 on 205 degrees of freedom
Multiple R-squared: 0.02363, Adjusted R-squared: 0.004582
F-statistic: 1.241 on 4 and 205 DF, p-value: 0.2949
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL9_rank
Effect size...............: 85.88781
Standard error............: 69.27534
Odds ratio (effect size)..: 1.998037e+37
Lower 95% CI..............: 0
Upper 95% CI..............: 1.85765e+96
T-value...................: 1.239804
P-value...................: 0.2164654
R^2.......................: 0.023634
Adjusted r^2..............: 0.004582
Sample size of AE DB......: 622
Sample size of model......: 210
Missing data %............: 66.23794
- processing IL10_rankfilter: removed 465 rows (75%), 157 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
364.7
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-507.6 -294.5 -182.4 -30.0 7527.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.495e+03 2.545e+03 -0.980 0.328
currentDF[, TRAIT] 4.006e+00 6.702e+01 0.060 0.952
Age 5.101e-02 7.975e+00 0.006 0.995
Gendermale 1.494e+02 1.545e+02 0.967 0.335
ORdate_epoch 2.213e-01 1.998e-01 1.108 0.270
Residual standard error: 805.3 on 152 degrees of freedom
Multiple R-squared: 0.01592, Adjusted R-squared: -0.009979
F-statistic: 0.6147 on 4 and 152 DF, p-value: 0.6527
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL10_rank
Effect size...............: 4.006071
Standard error............: 67.01937
Odds ratio (effect size)..: 54.931
Lower 95% CI..............: 0
Upper 95% CI..............: 6.135592e+58
T-value...................: 0.059775
P-value...................: 0.9524135
R^2.......................: 0.015918
Adjusted r^2..............: -0.009979
Sample size of AE DB......: 622
Sample size of model......: 157
Missing data %............: 74.75884
- processing IL12_rankfilter: removed 456 rows (73%), 166 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-3663.1511 0.3295
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-632.5 -370.0 -227.5 11.1 7805.1
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2802.6425 3017.9267 -0.929 0.354
currentDF[, TRAIT] -85.3166 80.6430 -1.058 0.292
Age -0.5392 9.7708 -0.055 0.956
Gendermale 187.2526 188.2130 0.995 0.321
ORdate_epoch 0.2517 0.2389 1.053 0.294
Residual standard error: 1009 on 161 degrees of freedom
Multiple R-squared: 0.02587, Adjusted R-squared: 0.001667
F-statistic: 1.069 on 4 and 161 DF, p-value: 0.3738
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL12_rank
Effect size...............: -85.31664
Standard error............: 80.64298
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 3.909721e+31
T-value...................: -1.057955
P-value...................: 0.2916607
R^2.......................: 0.025869
Adjusted r^2..............: 0.001667
Sample size of AE DB......: 622
Sample size of model......: 166
Missing data %............: 73.3119
- processing IL13_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
425.29 98.14
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-780.5 -346.9 -218.9 -47.9 7717.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3080.5475 2171.4739 -1.419 0.157
currentDF[, TRAIT] 91.8409 65.2408 1.408 0.161
Age 5.9940 7.9464 0.754 0.451
Gendermale 187.5593 149.4428 1.255 0.211
ORdate_epoch 0.2355 0.1676 1.405 0.161
Residual standard error: 974.8 on 225 degrees of freedom
Multiple R-squared: 0.02712, Adjusted R-squared: 0.009826
F-statistic: 1.568 on 4 and 225 DF, p-value: 0.1837
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL13_rank
Effect size...............: 91.84085
Standard error............: 65.24084
Odds ratio (effect size)..: 7.69086e+39
Lower 95% CI..............: 0
Upper 95% CI..............: 2.630844e+95
T-value...................: 1.40772
P-value...................: 0.160594
R^2.......................: 0.027122
Adjusted r^2..............: 0.009826
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing IL21_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-2634.8462 0.2436
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-678.8 -347.2 -223.3 -28.3 7786.2
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3169.4111 2174.9753 -1.457 0.146
currentDF[, TRAIT] 61.8405 65.1770 0.949 0.344
Age 5.3648 7.9431 0.675 0.500
Gendermale 189.9441 149.9580 1.267 0.207
ORdate_epoch 0.2459 0.1677 1.466 0.144
Residual standard error: 977.1 on 225 degrees of freedom
Multiple R-squared: 0.02246, Adjusted R-squared: 0.005086
F-statistic: 1.293 on 4 and 225 DF, p-value: 0.2738
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL21_rank
Effect size...............: 61.84047
Standard error............: 65.17701
Odds ratio (effect size)..: 7.194045e+26
Lower 95% CI..............: 0
Upper 95% CI..............: 2.171506e+82
T-value...................: 0.948808
P-value...................: 0.3437362
R^2.......................: 0.022464
Adjusted r^2..............: 0.005086
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing INFG_rankfilter: removed 449 rows (72%), 173 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
419.7
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-611.8 -350.8 -222.4 -60.2 7900.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3270.2779 2843.4237 -1.150 0.252
currentDF[, TRAIT] 30.4820 82.0106 0.372 0.711
Age 3.3330 9.4029 0.354 0.723
Gendermale 192.2499 187.3074 1.026 0.306
ORdate_epoch 0.2665 0.2237 1.191 0.235
Residual standard error: 994.7 on 168 degrees of freedom
Multiple R-squared: 0.01677, Adjusted R-squared: -0.006636
F-statistic: 0.7165 on 4 and 168 DF, p-value: 0.5817
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: INFG_rank
Effect size...............: 30.48197
Standard error............: 82.01055
Odds ratio (effect size)..: 1.730413e+13
Lower 95% CI..............: 0
Upper 95% CI..............: 1.11415e+83
T-value...................: 0.371683
P-value...................: 0.7105969
R^2.......................: 0.016774
Adjusted r^2..............: -0.006636
Sample size of AE DB......: 622
Sample size of model......: 173
Missing data %............: 72.18649
- processing TNFA_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
420.1
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-661.3 -366.2 -238.0 9.6 7785.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2824.3152 2988.9771 -0.945 0.346
currentDF[, TRAIT] -83.7855 79.9593 -1.048 0.296
Age 1.5029 9.7776 0.154 0.878
Gendermale 182.6437 185.5905 0.984 0.327
ORdate_epoch 0.2419 0.2357 1.026 0.306
Residual standard error: 1008 on 159 degrees of freedom
Multiple R-squared: 0.02372, Adjusted R-squared: -0.0008397
F-statistic: 0.9658 on 4 and 159 DF, p-value: 0.428
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: TNFA_rank
Effect size...............: -83.78554
Standard error............: 79.95933
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 4.733214e+31
T-value...................: -1.047852
P-value...................: 0.296297
R^2.......................: 0.023721
Adjusted r^2..............: -0.00084
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing MIF_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-2634.8462 0.2436
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-560.8 -349.2 -226.6 -8.3 7889.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3052.3167 2437.0785 -1.252 0.212
currentDF[, TRAIT] -13.0189 72.6523 -0.179 0.858
Age 4.6119 7.9339 0.581 0.562
Gendermale 200.8322 149.7902 1.341 0.181
ORdate_epoch 0.2400 0.1881 1.276 0.203
Residual standard error: 979 on 225 degrees of freedom
Multiple R-squared: 0.01869, Adjusted R-squared: 0.001248
F-statistic: 1.072 on 4 and 225 DF, p-value: 0.3714
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MIF_rank
Effect size...............: -13.01887
Standard error............: 72.65233
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 1.54486e+56
T-value...................: -0.179194
P-value...................: 0.8579465
R^2.......................: 0.018693
Adjusted r^2..............: 0.001248
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing MCP1_rankfilter: removed 394 rows (63%), 228 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-2593.9993 0.2406
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-574.9 -349.0 -229.9 -6.0 7904.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3198.9288 2239.7333 -1.428 0.155
currentDF[, TRAIT] -6.0970 67.0521 -0.091 0.928
Age 5.0368 8.0582 0.625 0.533
Gendermale 204.0845 151.7621 1.345 0.180
ORdate_epoch 0.2493 0.1721 1.448 0.149
Residual standard error: 983 on 223 degrees of freedom
Multiple R-squared: 0.01862, Adjusted R-squared: 0.001013
F-statistic: 1.058 on 4 and 223 DF, p-value: 0.3784
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MCP1_rank
Effect size...............: -6.097011
Standard error............: 67.05205
Odds ratio (effect size)..: 0.002
Lower 95% CI..............: 0
Upper 95% CI..............: 2.678922e+54
T-value...................: -0.09093
P-value...................: 0.9276302
R^2.......................: 0.018616
Adjusted r^2..............: 0.001013
Sample size of AE DB......: 622
Sample size of model......: 228
Missing data %............: 63.34405
- processing MIP1a_rankfilter: removed 408 rows (66%), 214 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
433.6
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-603.4 -344.6 -236.3 -22.3 7841.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2975.2993 2263.2006 -1.315 0.190
currentDF[, TRAIT] 29.1841 69.1324 0.422 0.673
Age 5.0826 8.4091 0.604 0.546
Gendermale 200.0214 160.8853 1.243 0.215
ORdate_epoch 0.2319 0.1739 1.333 0.184
Residual standard error: 1005 on 209 degrees of freedom
Multiple R-squared: 0.01766, Adjusted R-squared: -0.001136
F-statistic: 0.9396 on 4 and 209 DF, p-value: 0.4419
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MIP1a_rank
Effect size...............: 29.1841
Standard error............: 69.13239
Odds ratio (effect size)..: 4.726008e+12
Lower 95% CI..............: 0
Upper 95% CI..............: 3.320252e+71
T-value...................: 0.422148
P-value...................: 0.6733509
R^2.......................: 0.017665
Adjusted r^2..............: -0.001136
Sample size of AE DB......: 622
Sample size of model......: 214
Missing data %............: 65.59485
- processing RANTES_rankfilter: removed 396 rows (64%), 226 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-2570.6277 0.2388
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-660.1 -356.9 -220.9 8.3 7798.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4596.7225 2442.8131 -1.882 0.0612 .
currentDF[, TRAIT] 95.0634 73.3891 1.295 0.1966
Age 6.8155 8.1449 0.837 0.4036
Gendermale 201.8777 152.8473 1.321 0.1879
ORdate_epoch 0.3512 0.1855 1.893 0.0596 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 983.7 on 221 degrees of freedom
Multiple R-squared: 0.02553, Adjusted R-squared: 0.007895
F-statistic: 1.448 on 4 and 221 DF, p-value: 0.2193
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: RANTES_rank
Effect size...............: 95.06344
Standard error............: 73.38914
Odds ratio (effect size)..: 1.929866e+41
Lower 95% CI..............: 0
Upper 95% CI..............: 5.696677e+103
T-value...................: 1.295334
P-value...................: 0.1965565
R^2.......................: 0.025533
Adjusted r^2..............: 0.007895
Sample size of AE DB......: 622
Sample size of model......: 226
Missing data %............: 63.6656
- processing MIG_rankfilter: removed 395 rows (64%), 227 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
427.4 110.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-766.3 -343.3 -215.5 -17.8 7727.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2460.1144 2262.0638 -1.088 0.278
currentDF[, TRAIT] 93.8105 69.4114 1.352 0.178
Age 6.5666 8.0622 0.814 0.416
Gendermale 185.5187 151.9430 1.221 0.223
ORdate_epoch 0.1833 0.1765 1.039 0.300
Residual standard error: 981.4 on 222 degrees of freedom
Multiple R-squared: 0.02645, Adjusted R-squared: 0.008904
F-statistic: 1.508 on 4 and 222 DF, p-value: 0.2009
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MIG_rank
Effect size...............: 93.8105
Standard error............: 69.41138
Odds ratio (effect size)..: 5.512936e+40
Lower 95% CI..............: 0
Upper 95% CI..............: 6.691845e+99
T-value...................: 1.351515
P-value...................: 0.1779062
R^2.......................: 0.026445
Adjusted r^2..............: 0.008904
Sample size of AE DB......: 622
Sample size of model......: 227
Missing data %............: 63.50482
- processing IP10_rankfilter: removed 415 rows (67%), 207 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_epoch
-2928.7520 102.5032 232.7368 0.2545
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-825.8 -373.5 -210.4 -22.4 7555.3
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3404.8038 2330.4974 -1.461 0.146
currentDF[, TRAIT] 112.4819 71.7595 1.567 0.119
Age 7.0330 8.7606 0.803 0.423
Gendermale 233.5356 159.9559 1.460 0.146
ORdate_epoch 0.2544 0.1785 1.426 0.155
Residual standard error: 1014 on 202 degrees of freedom
Multiple R-squared: 0.0319, Adjusted R-squared: 0.01273
F-statistic: 1.664 on 4 and 202 DF, p-value: 0.1597
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IP10_rank
Effect size...............: 112.4819
Standard error............: 71.75947
Odds ratio (effect size)..: 7.083509e+48
Lower 95% CI..............: 0
Upper 95% CI..............: 8.573218e+109
T-value...................: 1.567484
P-value...................: 0.1185667
R^2.......................: 0.031901
Adjusted r^2..............: 0.01273
Sample size of AE DB......: 622
Sample size of model......: 207
Missing data %............: 66.72026
- processing Eotaxin1_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-2634.8462 0.2436
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-597.2 -344.0 -232.0 -10.9 7875.7
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3136.0606 2200.1422 -1.425 0.155
currentDF[, TRAIT] 23.3202 65.8930 0.354 0.724
Age 4.8489 7.9384 0.611 0.542
Gendermale 196.3442 150.3869 1.306 0.193
ORdate_epoch 0.2456 0.1699 1.446 0.150
Residual standard error: 978.8 on 225 degrees of freedom
Multiple R-squared: 0.0191, Adjusted R-squared: 0.001661
F-statistic: 1.095 on 4 and 225 DF, p-value: 0.3597
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 23.32025
Standard error............: 65.89295
Odds ratio (effect size)..: 13423141327
Lower 95% CI..............: 0
Upper 95% CI..............: 1.648422e+66
T-value...................: 0.353911
P-value...................: 0.7237369
R^2.......................: 0.019099
Adjusted r^2..............: 0.001661
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing TARC_rankfilter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_epoch
-3773.0185 114.3868 263.6155 0.3198
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-847.8 -400.3 -212.1 -12.6 7574.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4569.3095 2925.0326 -1.562 0.120
currentDF[, TRAIT] 125.7896 78.6581 1.599 0.111
Age 8.3111 9.0200 0.921 0.358
Gendermale 273.2925 169.4428 1.613 0.108
ORdate_epoch 0.3376 0.2209 1.528 0.128
Residual standard error: 1032 on 198 degrees of freedom
Multiple R-squared: 0.02888, Adjusted R-squared: 0.009263
F-statistic: 1.472 on 4 and 198 DF, p-value: 0.212
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: TARC_rank
Effect size...............: 125.7896
Standard error............: 78.65813
Odds ratio (effect size)..: 4.263086e+54
Lower 95% CI..............: 0
Upper 95% CI..............: 3.84485e+121
T-value...................: 1.599194
P-value...................: 0.1113721
R^2.......................: 0.028882
Adjusted r^2..............: 0.009263
Sample size of AE DB......: 622
Sample size of model......: 203
Missing data %............: 67.36334
- processing PARC_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-2634.8462 0.2436
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-641.1 -345.8 -226.9 -17.4 7776.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4245.2846 2314.9359 -1.834 0.0680 .
currentDF[, TRAIT] 85.2705 68.8847 1.238 0.2171
Age 5.3302 7.9166 0.673 0.5015
Gendermale 216.5889 149.7836 1.446 0.1496
ORdate_epoch 0.3301 0.1778 1.856 0.0647 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 975.7 on 225 degrees of freedom
Multiple R-squared: 0.02519, Adjusted R-squared: 0.007862
F-statistic: 1.454 on 4 and 225 DF, p-value: 0.2173
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: PARC_rank
Effect size...............: 85.27054
Standard error............: 68.88471
Odds ratio (effect size)..: 1.077769e+37
Lower 95% CI..............: 0
Upper 95% CI..............: 4.65991e+95
T-value...................: 1.237873
P-value...................: 0.2170532
R^2.......................: 0.025192
Adjusted r^2..............: 0.007862
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing MDC_rankfilter: removed 407 rows (65%), 215 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
442
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-619.2 -355.7 -242.4 -0.7 7834.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3425.4247 2396.2086 -1.430 0.154
currentDF[, TRAIT] 24.6877 72.9094 0.339 0.735
Age 5.6913 8.4719 0.672 0.502
Gendermale 223.5451 160.1485 1.396 0.164
ORdate_epoch 0.2637 0.1836 1.436 0.152
Residual standard error: 1010 on 210 degrees of freedom
Multiple R-squared: 0.01967, Adjusted R-squared: 0.0009959
F-statistic: 1.053 on 4 and 210 DF, p-value: 0.3807
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MDC_rank
Effect size...............: 24.68774
Standard error............: 72.90941
Odds ratio (effect size)..: 52692592722
Lower 95% CI..............: 0
Upper 95% CI..............: 6.074237e+72
T-value...................: 0.338608
P-value...................: 0.735243
R^2.......................: 0.019669
Adjusted r^2..............: 0.000996
Sample size of AE DB......: 622
Sample size of model......: 215
Missing data %............: 65.43408
- processing OPG_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-2525.1721 103.3467 0.2349
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-822.3 -343.3 -212.9 -33.5 7687.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3216.3921 2165.1723 -1.486 0.139
currentDF[, TRAIT] 105.4097 64.9337 1.623 0.106
Age 6.2121 7.9363 0.783 0.435
Gendermale 189.7141 149.0807 1.273 0.204
ORdate_epoch 0.2450 0.1669 1.468 0.143
Residual standard error: 973.4 on 225 degrees of freedom
Multiple R-squared: 0.02991, Adjusted R-squared: 0.01267
F-statistic: 1.735 on 4 and 225 DF, p-value: 0.1433
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: OPG_rank
Effect size...............: 105.4097
Standard error............: 64.9337
Odds ratio (effect size)..: 6.009686e+45
Lower 95% CI..............: 0
Upper 95% CI..............: 1.125979e+101
T-value...................: 1.623344
P-value...................: 0.1059162
R^2.......................: 0.029915
Adjusted r^2..............: 0.012669
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing sICAM1_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-2634.8462 0.2436
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-627.6 -354.7 -237.8 23.0 7897.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2684.1732 2242.3307 -1.197 0.233
currentDF[, TRAIT] -68.1091 66.9149 -1.018 0.310
Age 3.5039 7.9914 0.438 0.661
Gendermale 194.4293 149.5878 1.300 0.195
ORdate_epoch 0.2170 0.1715 1.265 0.207
Residual standard error: 976.8 on 225 degrees of freedom
Multiple R-squared: 0.02305, Adjusted R-squared: 0.005684
F-statistic: 1.327 on 4 and 225 DF, p-value: 0.2606
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: sICAM1_rank
Effect size...............: -68.10911
Standard error............: 66.91487
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 2.397033e+27
T-value...................: -1.017847
P-value...................: 0.3098438
R^2.......................: 0.023051
Adjusted r^2..............: 0.005684
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing VEGFA_rankfilter: removed 421 rows (68%), 201 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
411.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-580.6 -336.9 -215.9 -25.2 7971.1
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2906.4481 2483.2679 -1.170 0.243
currentDF[, TRAIT] -53.0233 73.4787 -0.722 0.471
Age 2.2669 8.1437 0.278 0.781
Gendermale 199.6167 151.9337 1.314 0.190
ORdate_epoch 0.2401 0.1952 1.230 0.220
Residual standard error: 932.9 on 196 degrees of freedom
Multiple R-squared: 0.01528, Adjusted R-squared: -0.004816
F-statistic: 0.7603 on 4 and 196 DF, p-value: 0.5523
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: VEGFA_rank
Effect size...............: -53.02325
Standard error............: 73.47875
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 3.30117e+39
T-value...................: -0.721613
P-value...................: 0.4713921
R^2.......................: 0.01528
Adjusted r^2..............: -0.004816
Sample size of AE DB......: 622
Sample size of model......: 201
Missing data %............: 67.68489
- processing TGFB_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-2670.9926 0.2468
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-582.8 -351.6 -228.6 -12.2 7900.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3255.4089 2191.7975 -1.485 0.139
currentDF[, TRAIT] -1.5566 65.7843 -0.024 0.981
Age 4.9552 7.9296 0.625 0.533
Gendermale 196.0778 147.6988 1.328 0.186
ORdate_epoch 0.2550 0.1693 1.507 0.133
Residual standard error: 976.9 on 226 degrees of freedom
Multiple R-squared: 0.01905, Adjusted R-squared: 0.001685
F-statistic: 1.097 on 4 and 226 DF, p-value: 0.3588
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: TGFB_rank
Effect size...............: -1.556598
Standard error............: 65.78433
Odds ratio (effect size)..: 0.211
Lower 95% CI..............: 0
Upper 95% CI..............: 2.09283e+55
T-value...................: -0.023662
P-value...................: 0.981143
R^2.......................: 0.019047
Adjusted r^2..............: 0.001685
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP2_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
396.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-501.3 -317.0 -208.8 -25.7 7945.2
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2050.3944 2013.2917 -1.018 0.310
currentDF[, TRAIT] 5.5043 59.8739 0.092 0.927
Age 3.3422 7.1319 0.469 0.640
Gendermale 171.0026 134.8025 1.269 0.206
ORdate_epoch 0.1671 0.1568 1.066 0.288
Residual standard error: 882.3 on 226 degrees of freedom
Multiple R-squared: 0.01293, Adjusted R-squared: -0.004539
F-statistic: 0.7402 on 4 and 226 DF, p-value: 0.5655
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MMP2_rank
Effect size...............: 5.504269
Standard error............: 59.87385
Odds ratio (effect size)..: 245.739
Lower 95% CI..............: 0
Upper 95% CI..............: 2.270536e+53
T-value...................: 0.091931
P-value...................: 0.9268342
R^2.......................: 0.012932
Adjusted r^2..............: -0.004539
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP8_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
396.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-506.7 -321.5 -205.6 -19.7 7938.7
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2032.3328 1980.2522 -1.026 0.306
currentDF[, TRAIT] -18.9107 58.5973 -0.323 0.747
Age 3.2413 7.1032 0.456 0.649
Gendermale 173.9266 133.9810 1.298 0.196
ORdate_epoch 0.1660 0.1549 1.072 0.285
Residual standard error: 882.1 on 226 degrees of freedom
Multiple R-squared: 0.01335, Adjusted R-squared: -0.004113
F-statistic: 0.7644 on 4 and 226 DF, p-value: 0.5494
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MMP8_rank
Effect size...............: -18.91074
Standard error............: 58.59728
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 4.63635e+41
T-value...................: -0.322724
P-value...................: 0.7472027
R^2.......................: 0.013349
Adjusted r^2..............: -0.004113
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP9_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
396.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-503.8 -315.7 -210.8 -25.5 7948.1
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2085.3780 1991.7887 -1.047 0.296
currentDF[, TRAIT] 18.2990 58.5599 0.312 0.755
Age 3.3159 7.1028 0.467 0.641
Gendermale 169.3323 133.1398 1.272 0.205
ORdate_epoch 0.1701 0.1557 1.092 0.276
Residual standard error: 882.1 on 226 degrees of freedom
Multiple R-squared: 0.01332, Adjusted R-squared: -0.004142
F-statistic: 0.7628 on 4 and 226 DF, p-value: 0.5505
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MMP9_rank
Effect size...............: 18.29904
Standard error............: 58.55988
Odds ratio (effect size)..: 88547027
Lower 95% CI..............: 0
Upper 95% CI..............: 6.228007e+57
T-value...................: 0.312484
P-value...................: 0.7549606
R^2.......................: 0.013321
Adjusted r^2..............: -0.004142
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
Analysis of LDLR.
- processing IL2_rankfilter: removed 440 rows (71%), 182 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
232.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-252.3 -136.2 -69.5 25.9 3233.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 74.95626 871.41591 0.086 0.932
currentDF[, TRAIT] -27.23453 24.18379 -1.126 0.262
Age -0.99300 2.91937 -0.340 0.734
Gendermale 35.88049 56.63337 0.634 0.527
ORdate_epoch 0.01590 0.06893 0.231 0.818
Residual standard error: 319.6 on 177 degrees of freedom
Multiple R-squared: 0.01195, Adjusted R-squared: -0.01038
F-statistic: 0.5353 on 4 and 177 DF, p-value: 0.71
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL2_rank
Effect size...............: -27.23454
Standard error............: 24.18379
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 572597648
T-value...................: -1.126148
P-value...................: 0.2616273
R^2.......................: 0.011953
Adjusted r^2..............: -0.010376
Sample size of AE DB......: 622
Sample size of model......: 182
Missing data %............: 70.73955
- processing IL4_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
232.3
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-238.5 -146.5 -68.5 19.3 3255.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -109.46381 1000.61883 -0.109 0.913
currentDF[, TRAIT] -11.73571 27.09196 -0.433 0.665
Age -1.05572 3.24258 -0.326 0.745
Gendermale 49.59202 63.60564 0.780 0.437
ORdate_epoch 0.03027 0.07942 0.381 0.704
Residual standard error: 335.7 on 159 degrees of freedom
Multiple R-squared: 0.007739, Adjusted R-squared: -0.01722
F-statistic: 0.31 on 4 and 159 DF, p-value: 0.871
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL4_rank
Effect size...............: -11.73571
Standard error............: 27.09196
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 9.212675e+17
T-value...................: -0.43318
P-value...................: 0.6654707
R^2.......................: 0.007739
Adjusted r^2..............: -0.017224
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing IL5_rankfilter: removed 443 rows (71%), 179 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
228.4 -39.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-260.4 -136.8 -64.0 28.5 3218.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 61.97559 899.99742 0.069 0.945
currentDF[, TRAIT] -37.99062 24.55770 -1.547 0.124
Age -1.44835 2.96753 -0.488 0.626
Gendermale 40.75431 57.66776 0.707 0.481
ORdate_epoch 0.01879 0.07214 0.260 0.795
Residual standard error: 320.4 on 174 degrees of freedom
Multiple R-squared: 0.01986, Adjusted R-squared: -0.002668
F-statistic: 0.8816 on 4 and 174 DF, p-value: 0.4762
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL5_rank
Effect size...............: -37.99062
Standard error............: 24.55769
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 25398.87
T-value...................: -1.546995
P-value...................: 0.1236814
R^2.......................: 0.019864
Adjusted r^2..............: -0.002668
Sample size of AE DB......: 622
Sample size of model......: 179
Missing data %............: 71.22186
- processing IL6_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
228.5 -41.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-294.8 -135.9 -61.2 29.3 3200.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -254.51799 784.26410 -0.325 0.746
currentDF[, TRAIT] -41.88768 23.39192 -1.791 0.075 .
Age -1.38282 2.83222 -0.488 0.626
Gendermale 31.36037 52.78732 0.594 0.553
ORdate_epoch 0.04441 0.06259 0.709 0.479
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 312.8 on 184 degrees of freedom
Multiple R-squared: 0.02331, Adjusted R-squared: 0.002074
F-statistic: 1.098 on 4 and 184 DF, p-value: 0.3592
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL6_rank
Effect size...............: -41.88768
Standard error............: 23.39192
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 52.482
T-value...................: -1.79069
P-value...................: 0.07498695
R^2.......................: 0.023306
Adjusted r^2..............: 0.002074
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing IL8_rankfilter: removed 442 rows (71%), 180 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
237.52 37.91
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-261.4 -145.0 -57.4 49.5 3239.1
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 275.307244 839.704655 0.328 0.743
currentDF[, TRAIT] 37.066695 25.063024 1.479 0.141
Age -0.108796 2.867861 -0.038 0.970
Gendermale 25.027043 57.068584 0.439 0.662
ORdate_epoch -0.003977 0.066748 -0.060 0.953
Residual standard error: 321.3 on 175 degrees of freedom
Multiple R-squared: 0.01511, Adjusted R-squared: -0.007406
F-statistic: 0.671 on 4 and 175 DF, p-value: 0.6129
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL8_rank
Effect size...............: 37.0667
Standard error............: 25.06302
Odds ratio (effect size)..: 1.25274e+16
Lower 95% CI..............: 0
Upper 95% CI..............: 2.703569e+37
T-value...................: 1.478939
P-value...................: 0.1409543
R^2.......................: 0.015106
Adjusted r^2..............: -0.007406
Sample size of AE DB......: 622
Sample size of model......: 180
Missing data %............: 71.06109
- processing IL9_rankfilter: removed 412 rows (66%), 210 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
238.1 43.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-354.8 -136.5 -65.9 21.7 3203.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -310.61239 694.23303 -0.447 0.6550
currentDF[, TRAIT] 43.63537 21.35138 2.044 0.0423 *
Age -0.30010 2.63417 -0.114 0.9094
Gendermale 47.80791 49.08471 0.974 0.3312
ORdate_epoch 0.04240 0.05315 0.798 0.4259
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 306.4 on 205 degrees of freedom
Multiple R-squared: 0.02714, Adjusted R-squared: 0.008156
F-statistic: 1.43 on 4 and 205 DF, p-value: 0.2254
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL9_rank
Effect size...............: 43.63537
Standard error............: 21.35138
Odds ratio (effect size)..: 8.924799e+18
Lower 95% CI..............: 5.969
Upper 95% CI..............: 1.334322e+37
T-value...................: 2.043679
P-value...................: 0.04226425
R^2.......................: 0.027138
Adjusted r^2..............: 0.008156
Sample size of AE DB......: 622
Sample size of model......: 210
Missing data %............: 66.23794
- processing IL10_rankfilter: removed 465 rows (75%), 157 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
230.7
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-235.9 -150.1 -72.8 16.1 3239.2
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 61.59955 1081.26283 0.057 0.955
currentDF[, TRAIT] -21.27182 28.47412 -0.747 0.456
Age -0.85351 3.38808 -0.252 0.801
Gendermale 45.48144 65.65281 0.693 0.490
ORdate_epoch 0.01550 0.08487 0.183 0.855
Residual standard error: 342.1 on 152 degrees of freedom
Multiple R-squared: 0.008988, Adjusted R-squared: -0.01709
F-statistic: 0.3446 on 4 and 152 DF, p-value: 0.8474
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL10_rank
Effect size...............: -21.27182
Standard error............: 28.47412
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 9.986761e+14
T-value...................: -0.747058
P-value...................: 0.4561825
R^2.......................: 0.008988
Adjusted r^2..............: -0.017091
Sample size of AE DB......: 622
Sample size of model......: 157
Missing data %............: 74.75884
- processing IL12_rankfilter: removed 456 rows (73%), 166 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
235.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-246.0 -144.9 -69.7 48.2 3234.2
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.04500 994.18011 0.052 0.958
currentDF[, TRAIT] -21.34594 26.56580 -0.804 0.423
Age -1.15725 3.21875 -0.360 0.720
Gendermale 47.84764 62.00203 0.772 0.441
ORdate_epoch 0.01819 0.07870 0.231 0.817
Residual standard error: 332.5 on 161 degrees of freedom
Multiple R-squared: 0.009725, Adjusted R-squared: -0.01488
F-statistic: 0.3953 on 4 and 161 DF, p-value: 0.8118
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL12_rank
Effect size...............: -21.34594
Standard error............: 26.5658
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 2.202143e+13
T-value...................: -0.803512
P-value...................: 0.4228636
R^2.......................: 0.009725
Adjusted r^2..............: -0.014878
Sample size of AE DB......: 622
Sample size of model......: 166
Missing data %............: 73.3119
- processing IL13_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
240.2
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-285.7 -139.4 -71.6 34.5 3232.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -222.29490 703.29669 -0.316 0.752
currentDF[, TRAIT] 23.71734 21.13019 1.122 0.263
Age 0.92072 2.57367 0.358 0.721
Gendermale 17.75161 48.40153 0.367 0.714
ORdate_epoch 0.03079 0.05429 0.567 0.571
Residual standard error: 315.7 on 225 degrees of freedom
Multiple R-squared: 0.008443, Adjusted R-squared: -0.009185
F-statistic: 0.479 on 4 and 225 DF, p-value: 0.7512
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL13_rank
Effect size...............: 23.71734
Standard error............: 21.13019
Odds ratio (effect size)..: 19966909475
Lower 95% CI..............: 0
Upper 95% CI..............: 1.93506e+28
T-value...................: 1.122438
P-value...................: 0.2628724
R^2.......................: 0.008443
Adjusted r^2..............: -0.009185
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing IL21_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
240.2
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-278.9 -139.0 -71.9 34.8 3234.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -234.61656 702.64078 -0.334 0.739
currentDF[, TRAIT] 24.28831 21.05588 1.154 0.250
Age 0.85061 2.56608 0.331 0.741
Gendermale 16.84232 48.44496 0.348 0.728
ORdate_epoch 0.03220 0.05419 0.594 0.553
Residual standard error: 315.7 on 225 degrees of freedom
Multiple R-squared: 0.008753, Adjusted R-squared: -0.008869
F-statistic: 0.4967 on 4 and 225 DF, p-value: 0.7382
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL21_rank
Effect size...............: 24.28831
Standard error............: 21.05588
Odds ratio (effect size)..: 35341101619
Lower 95% CI..............: 0
Upper 95% CI..............: 2.960796e+28
T-value...................: 1.153517
P-value...................: 0.2499222
R^2.......................: 0.008753
Adjusted r^2..............: -0.008869
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing INFG_rankfilter: removed 449 rows (72%), 173 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
237.8
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-238.1 -143.7 -72.2 34.9 3257.7
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -56.02598 936.57885 -0.060 0.952
currentDF[, TRAIT] -4.82446 27.01298 -0.179 0.858
Age -1.27016 3.09715 -0.410 0.682
Gendermale 44.88360 61.69610 0.727 0.468
ORdate_epoch 0.02773 0.07368 0.376 0.707
Residual standard error: 327.6 on 168 degrees of freedom
Multiple R-squared: 0.006139, Adjusted R-squared: -0.01752
F-statistic: 0.2594 on 4 and 168 DF, p-value: 0.9036
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: INFG_rank
Effect size...............: -4.824462
Standard error............: 27.01298
Odds ratio (effect size)..: 0.008
Lower 95% CI..............: 0
Upper 95% CI..............: 7.919121e+20
T-value...................: -0.178598
P-value...................: 0.8584686
R^2.......................: 0.006139
Adjusted r^2..............: -0.017524
Sample size of AE DB......: 622
Sample size of model......: 173
Missing data %............: 72.18649
- processing TNFA_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
232.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-245.8 -148.2 -70.9 31.9 3229.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 171.612984 993.514059 0.173 0.863
currentDF[, TRAIT] -26.408125 26.577894 -0.994 0.322
Age -1.300014 3.250018 -0.400 0.690
Gendermale 44.784778 61.688911 0.726 0.469
ORdate_epoch 0.009309 0.078342 0.119 0.906
Residual standard error: 334.9 on 159 degrees of freedom
Multiple R-squared: 0.01126, Adjusted R-squared: -0.01361
F-statistic: 0.4528 on 4 and 159 DF, p-value: 0.7702
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: TNFA_rank
Effect size...............: -26.40812
Standard error............: 26.57789
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 142776613871
T-value...................: -0.993612
P-value...................: 0.3219213
R^2.......................: 0.011263
Adjusted r^2..............: -0.013611
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing MIF_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
240.2
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-252.2 -138.4 -64.9 39.3 3205.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -753.67740 784.78307 -0.960 0.338
currentDF[, TRAIT] 32.40054 23.39536 1.385 0.167
Age 0.74542 2.55486 0.292 0.771
Gendermale 22.41611 48.23514 0.465 0.643
ORdate_epoch 0.07376 0.06056 1.218 0.224
Residual standard error: 315.2 on 225 degrees of freedom
Multiple R-squared: 0.01132, Adjusted R-squared: -0.006258
F-statistic: 0.644 on 4 and 225 DF, p-value: 0.6317
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MIF_rank
Effect size...............: 32.40054
Standard error............: 23.39536
Odds ratio (effect size)..: 1.178623e+14
Lower 95% CI..............: 0
Upper 95% CI..............: 9.680641e+33
T-value...................: 1.384913
P-value...................: 0.1674507
R^2.......................: 0.011319
Adjusted r^2..............: -0.006258
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing MCP1_rankfilter: removed 394 rows (63%), 228 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
241.3 29.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-284.2 -136.6 -62.4 19.8 3231.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -499.18417 719.87579 -0.693 0.489
currentDF[, TRAIT] 33.95320 21.55129 1.575 0.117
Age 1.18539 2.58999 0.458 0.648
Gendermale 17.88480 48.77808 0.367 0.714
ORdate_epoch 0.05148 0.05532 0.931 0.353
Residual standard error: 315.9 on 223 degrees of freedom
Multiple R-squared: 0.01394, Adjusted R-squared: -0.003746
F-statistic: 0.7882 on 4 and 223 DF, p-value: 0.5339
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MCP1_rank
Effect size...............: 33.9532
Standard error............: 21.55129
Odds ratio (effect size)..: 5.567856e+14
Lower 95% CI..............: 0
Upper 95% CI..............: 1.231739e+33
T-value...................: 1.57546
P-value...................: 0.1165679
R^2.......................: 0.013941
Adjusted r^2..............: -0.003746
Sample size of AE DB......: 622
Sample size of model......: 228
Missing data %............: 63.34405
- processing MIP1a_rankfilter: removed 408 rows (66%), 214 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
237.19 32.28
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-303.5 -133.0 -59.9 25.0 3233.2
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -223.39329 687.23876 -0.325 0.745
currentDF[, TRAIT] 30.69566 20.99260 1.462 0.145
Age -0.41957 2.55350 -0.164 0.870
Gendermale 44.68521 48.85410 0.915 0.361
ORdate_epoch 0.03621 0.05281 0.686 0.494
Residual standard error: 305.3 on 209 degrees of freedom
Multiple R-squared: 0.01707, Adjusted R-squared: -0.001739
F-statistic: 0.9075 on 4 and 209 DF, p-value: 0.4604
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MIP1a_rank
Effect size...............: 30.69566
Standard error............: 20.9926
Odds ratio (effect size)..: 2.142678e+13
Lower 95% CI..............: 0
Upper 95% CI..............: 1.585681e+31
T-value...................: 1.462214
P-value...................: 0.1451847
R^2.......................: 0.017073
Adjusted r^2..............: -0.001739
Sample size of AE DB......: 622
Sample size of model......: 214
Missing data %............: 65.59485
- processing RANTES_rankfilter: removed 396 rows (64%), 226 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
240.7
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-248.9 -144.7 -70.4 22.9 3260.3
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -566.25323 791.57888 -0.715 0.475
currentDF[, TRAIT] 20.17020 23.78131 0.848 0.397
Age 1.06379 2.63930 0.403 0.687
Gendermale 22.52060 49.52925 0.455 0.650
ORdate_epoch 0.05713 0.06010 0.951 0.343
Residual standard error: 318.8 on 221 degrees of freedom
Multiple R-squared: 0.006251, Adjusted R-squared: -0.01174
F-statistic: 0.3475 on 4 and 221 DF, p-value: 0.8456
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: RANTES_rank
Effect size...............: 20.1702
Standard error............: 23.78131
Odds ratio (effect size)..: 575183172
Lower 95% CI..............: 0
Upper 95% CI..............: 1.006617e+29
T-value...................: 0.848153
P-value...................: 0.3972702
R^2.......................: 0.006251
Adjusted r^2..............: -0.011736
Sample size of AE DB......: 622
Sample size of model......: 226
Missing data %............: 63.6656
- processing MIG_rankfilter: removed 395 rows (64%), 227 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
239.26 38.66
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-317.9 -138.4 -61.4 30.3 3230.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.004e+01 7.280e+02 0.069 0.945
currentDF[, TRAIT] 3.916e+01 2.234e+01 1.753 0.081 .
Age 1.319e+00 2.595e+00 0.508 0.612
Gendermale 1.461e+01 4.890e+01 0.299 0.765
ORdate_epoch 7.073e-03 5.679e-02 0.125 0.901
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 315.8 on 222 degrees of freedom
Multiple R-squared: 0.0166, Adjusted R-squared: -0.001118
F-statistic: 0.9369 on 4 and 222 DF, p-value: 0.4433
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MIG_rank
Effect size...............: 39.15692
Standard error............: 22.33913
Odds ratio (effect size)..: 1.013061e+17
Lower 95% CI..............: 0.01
Upper 95% CI..............: 1.049755e+36
T-value...................: 1.75284
P-value...................: 0.08100985
R^2.......................: 0.016601
Adjusted r^2..............: -0.001118
Sample size of AE DB......: 622
Sample size of model......: 227
Missing data %............: 63.50482
- processing IP10_rankfilter: removed 415 rows (67%), 207 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
236.65 36.44
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-322.9 -135.3 -58.1 23.4 3236.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -376.89831 711.07176 -0.530 0.597
currentDF[, TRAIT] 36.04478 21.89495 1.646 0.101
Age -0.12584 2.67300 -0.047 0.962
Gendermale 56.69334 48.80510 1.162 0.247
ORdate_epoch 0.04623 0.05445 0.849 0.397
Residual standard error: 309.3 on 202 degrees of freedom
Multiple R-squared: 0.02317, Adjusted R-squared: 0.003827
F-statistic: 1.198 on 4 and 202 DF, p-value: 0.313
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IP10_rank
Effect size...............: 36.04478
Standard error............: 21.89495
Odds ratio (effect size)..: 4.508673e+15
Lower 95% CI..............: 0.001
Upper 95% CI..............: 1.956184e+34
T-value...................: 1.64626
P-value...................: 0.1012652
R^2.......................: 0.02317
Adjusted r^2..............: 0.003827
Sample size of AE DB......: 622
Sample size of model......: 207
Missing data %............: 66.72026
- processing Eotaxin1_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
240.2
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-268.0 -140.0 -66.7 31.0 3247.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -167.48409 710.19919 -0.236 0.814
currentDF[, TRAIT] 20.37463 21.27004 0.958 0.339
Age 0.73021 2.56248 0.285 0.776
Gendermale 16.98106 48.54442 0.350 0.727
ORdate_epoch 0.02750 0.05484 0.501 0.617
Residual standard error: 315.9 on 225 degrees of freedom
Multiple R-squared: 0.006941, Adjusted R-squared: -0.01071
F-statistic: 0.3931 on 4 and 225 DF, p-value: 0.8135
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 20.37463
Standard error............: 21.27004
Odds ratio (effect size)..: 705651252
Lower 95% CI..............: 0
Upper 95% CI..............: 8.99533e+26
T-value...................: 0.957903
P-value...................: 0.33914
R^2.......................: 0.006941
Adjusted r^2..............: -0.010714
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing TARC_rankfilter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
246.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-309.7 -132.2 -73.6 20.8 3231.1
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -684.42514 938.36026 -0.729 0.467
currentDF[, TRAIT] 36.38124 25.23379 1.442 0.151
Age 2.05783 2.89365 0.711 0.478
Gendermale 42.84827 54.35782 0.788 0.431
ORdate_epoch 0.06013 0.07088 0.848 0.397
Residual standard error: 330.9 on 198 degrees of freedom
Multiple R-squared: 0.01398, Adjusted R-squared: -0.00594
F-statistic: 0.7018 on 4 and 198 DF, p-value: 0.5916
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: TARC_rank
Effect size...............: 36.38124
Standard error............: 25.23379
Odds ratio (effect size)..: 6.312078e+15
Lower 95% CI..............: 0
Upper 95% CI..............: 1.903752e+37
T-value...................: 1.441767
P-value...................: 0.150948
R^2.......................: 0.01398
Adjusted r^2..............: -0.00594
Sample size of AE DB......: 622
Sample size of model......: 203
Missing data %............: 67.36334
- processing PARC_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
240.2
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-246.9 -133.7 -69.6 34.3 3212.3
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -577.60233 748.72980 -0.771 0.441
currentDF[, TRAIT] 26.68424 22.27968 1.198 0.232
Age 0.78495 2.56049 0.307 0.759
Gendermale 26.08549 48.44517 0.538 0.591
ORdate_epoch 0.05931 0.05751 1.031 0.304
Residual standard error: 315.6 on 225 degrees of freedom
Multiple R-squared: 0.009208, Adjusted R-squared: -0.008407
F-statistic: 0.5227 on 4 and 225 DF, p-value: 0.7191
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: PARC_rank
Effect size...............: 26.68424
Standard error............: 22.27968
Odds ratio (effect size)..: 387986757915
Lower 95% CI..............: 0
Upper 95% CI..............: 3.578209e+30
T-value...................: 1.197694
P-value...................: 0.2322964
R^2.......................: 0.009208
Adjusted r^2..............: -0.008407
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing MDC_rankfilter: removed 407 rows (65%), 215 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
236.52 31.03
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-330.4 -133.0 -62.5 40.2 3203.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -654.99360 721.24872 -0.908 0.3648
currentDF[, TRAIT] 39.58326 21.94543 1.804 0.0727 .
Age -0.20771 2.54999 -0.081 0.9352
Gendermale 55.01229 48.20403 1.141 0.2551
ORdate_epoch 0.06875 0.05527 1.244 0.2149
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 303.9 on 210 degrees of freedom
Multiple R-squared: 0.02284, Adjusted R-squared: 0.004231
F-statistic: 1.227 on 4 and 210 DF, p-value: 0.3003
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MDC_rank
Effect size...............: 39.58326
Standard error............: 21.94543
Odds ratio (effect size)..: 1.551636e+17
Lower 95% CI..............: 0.032
Upper 95% CI..............: 7.43213e+35
T-value...................: 1.803713
P-value...................: 0.07270942
R^2.......................: 0.022844
Adjusted r^2..............: 0.004231
Sample size of AE DB......: 622
Sample size of model......: 215
Missing data %............: 65.43408
- processing OPG_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
240.2
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-236.2 -139.4 -67.6 25.7 3266.3
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -268.38618 703.94227 -0.381 0.703
currentDF[, TRAIT] -9.02309 21.11129 -0.427 0.669
Age 0.44954 2.58026 0.174 0.862
Gendermale 22.28581 48.46923 0.460 0.646
ORdate_epoch 0.03673 0.05427 0.677 0.499
Residual standard error: 316.5 on 225 degrees of freedom
Multiple R-squared: 0.0037, Adjusted R-squared: -0.01401
F-statistic: 0.2089 on 4 and 225 DF, p-value: 0.9333
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: OPG_rank
Effect size...............: -9.023088
Standard error............: 21.11129
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 1.126194e+14
T-value...................: -0.427406
P-value...................: 0.6694925
R^2.......................: 0.0037
Adjusted r^2..............: -0.014012
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing sICAM1_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
240.2
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-292.8 -141.1 -71.9 30.4 3234.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -460.58071 724.85389 -0.635 0.526
currentDF[, TRAIT] 23.53057 21.63084 1.088 0.278
Age 0.98648 2.58328 0.382 0.703
Gendermale 23.66320 48.35561 0.489 0.625
ORdate_epoch 0.04905 0.05545 0.885 0.377
Residual standard error: 315.8 on 225 degrees of freedom
Multiple R-squared: 0.008108, Adjusted R-squared: -0.009526
F-statistic: 0.4598 on 4 and 225 DF, p-value: 0.7652
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: sICAM1_rank
Effect size...............: 23.53057
Standard error............: 21.63084
Odds ratio (effect size)..: 16565204929
Lower 95% CI..............: 0
Upper 95% CI..............: 4.28294e+28
T-value...................: 1.087825
P-value...................: 0.2778363
R^2.......................: 0.008108
Adjusted r^2..............: -0.009526
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing VEGFA_rankfilter: removed 421 rows (68%), 201 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
243.8
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-240.1 -145.3 -72.1 30.4 3260.7
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -273.02825 884.59455 -0.309 0.758
currentDF[, TRAIT] 3.96145 26.17474 0.151 0.880
Age 1.61071 2.90096 0.555 0.579
Gendermale 16.99322 54.12213 0.314 0.754
ORdate_epoch 0.03145 0.06952 0.452 0.651
Residual standard error: 332.3 on 196 degrees of freedom
Multiple R-squared: 0.003598, Adjusted R-squared: -0.01674
F-statistic: 0.1769 on 4 and 196 DF, p-value: 0.9501
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: VEGFA_rank
Effect size...............: 3.961448
Standard error............: 26.17474
Odds ratio (effect size)..: 52.533
Lower 95% CI..............: 0
Upper 95% CI..............: 1.001905e+24
T-value...................: 0.151346
P-value...................: 0.8798583
R^2.......................: 0.003598
Adjusted r^2..............: -0.016737
Sample size of AE DB......: 622
Sample size of model......: 201
Missing data %............: 67.68489
- processing TGFB_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
243.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-243.5 -136.5 -71.5 30.9 3259.1
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -332.15825 709.47779 -0.468 0.640
currentDF[, TRAIT] 7.41869 21.29418 0.348 0.728
Age 0.94209 2.56678 0.367 0.714
Gendermale 12.56489 47.80964 0.263 0.793
ORdate_epoch 0.04001 0.05479 0.730 0.466
Residual standard error: 316.2 on 226 degrees of freedom
Multiple R-squared: 0.003353, Adjusted R-squared: -0.01429
F-statistic: 0.1901 on 4 and 226 DF, p-value: 0.9434
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: TGFB_rank
Effect size...............: 7.418691
Standard error............: 21.29418
Odds ratio (effect size)..: 1666.85
Lower 95% CI..............: 0
Upper 95% CI..............: 2.227745e+21
T-value...................: 0.348391
P-value...................: 0.7278712
R^2.......................: 0.003353
Adjusted r^2..............: -0.014286
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP2_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
235.2
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-265.6 -140.3 -59.5 44.7 3258.7
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 66.90759 709.64445 0.094 0.925
currentDF[, TRAIT] -24.85362 21.10432 -1.178 0.240
Age 0.39686 2.51386 0.158 0.875
Gendermale 9.85885 47.51514 0.207 0.836
ORdate_epoch 0.01069 0.05525 0.194 0.847
Residual standard error: 311 on 226 degrees of freedom
Multiple R-squared: 0.00773, Adjusted R-squared: -0.009832
F-statistic: 0.4402 on 4 and 226 DF, p-value: 0.7795
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MMP2_rank
Effect size...............: -24.85362
Standard error............: 21.10432
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 14810435
T-value...................: -1.177656
P-value...................: 0.2401725
R^2.......................: 0.00773
Adjusted r^2..............: -0.009832
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP8_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
235.16 47.97
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-300.2 -132.6 -59.7 50.1 3246.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -45.40284 692.04620 -0.066 0.9477
currentDF[, TRAIT] 47.64552 20.47821 2.327 0.0209 *
Age 0.76794 2.48237 0.309 0.7573
Gendermale 6.33712 46.82286 0.135 0.8925
ORdate_epoch 0.01786 0.05412 0.330 0.7417
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 308.3 on 226 degrees of freedom
Multiple R-squared: 0.02499, Adjusted R-squared: 0.007738
F-statistic: 1.448 on 4 and 226 DF, p-value: 0.219
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MMP8_rank
Effect size...............: 47.64552
Standard error............: 20.47821
Odds ratio (effect size)..: 4.922508e+20
Lower 95% CI..............: 1822.979
Upper 95% CI..............: 1.329202e+38
T-value...................: 2.326645
P-value...................: 0.02086943
R^2.......................: 0.024995
Adjusted r^2..............: 0.007738
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP9_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
235.16 50.18
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-279.9 -137.5 -60.2 36.1 3222.2
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -278.05859 694.53100 -0.400 0.6893
currentDF[, TRAIT] 51.81355 20.41966 2.537 0.0118 *
Age 0.75234 2.47673 0.304 0.7616
Gendermale 19.28365 46.42546 0.415 0.6783
ORdate_epoch 0.03574 0.05430 0.658 0.5111
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 307.6 on 226 degrees of freedom
Multiple R-squared: 0.0293, Adjusted R-squared: 0.01211
F-statistic: 1.705 on 4 and 226 DF, p-value: 0.1497
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MMP9_rank
Effect size...............: 51.81355
Standard error............: 20.41966
Odds ratio (effect size)..: 3.179361e+22
Lower 95% CI..............: 132060.5
Upper 95% CI..............: 7.654322e+39
T-value...................: 2.537434
P-value...................: 0.0118407
R^2.......................: 0.029296
Adjusted r^2..............: 0.012115
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
Analysis of CD36.
- processing IL2_rankfilter: removed 440 rows (71%), 182 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-843.06851 0.07936
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-190.22 -105.31 -51.46 19.81 945.27
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -872.01033 510.21791 -1.709 0.0892 .
currentDF[, TRAIT] -5.92588 14.15972 -0.419 0.6761
Age 0.95185 1.70930 0.557 0.5783
Gendermale -3.03543 33.15909 -0.092 0.9272
ORdate_epoch 0.07668 0.04036 1.900 0.0591 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 187.1 on 177 degrees of freedom
Multiple R-squared: 0.0246, Adjusted R-squared: 0.00256
F-statistic: 1.116 on 4 and 177 DF, p-value: 0.3505
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL2_rank
Effect size...............: -5.925881
Standard error............: 14.15972
Odds ratio (effect size)..: 0.003
Lower 95% CI..............: 0
Upper 95% CI..............: 3015900123
T-value...................: -0.418503
P-value...................: 0.6760869
R^2.......................: 0.024603
Adjusted r^2..............: 0.00256
Sample size of AE DB......: 622
Sample size of model......: 182
Missing data %............: 70.73955
- processing IL4_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-1.048e+03 9.619e-02
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-196.14 -113.68 -56.58 14.42 949.91
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.136e+03 5.802e+02 -1.958 0.0519 .
currentDF[, TRAIT] 5.007e+00 1.571e+01 0.319 0.7503
Age 1.196e+00 1.880e+00 0.636 0.5255
Gendermale 1.036e+00 3.688e+01 0.028 0.9776
ORdate_epoch 9.670e-02 4.605e-02 2.100 0.0373 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 194.7 on 159 degrees of freedom
Multiple R-squared: 0.03023, Adjusted R-squared: 0.00583
F-statistic: 1.239 on 4 and 159 DF, p-value: 0.2966
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL4_rank
Effect size...............: 5.006842
Standard error............: 15.70842
Odds ratio (effect size)..: 149.432
Lower 95% CI..............: 0
Upper 95% CI..............: 3.513341e+15
T-value...................: 0.318736
P-value...................: 0.7503449
R^2.......................: 0.030227
Adjusted r^2..............: 0.00583
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing IL5_rankfilter: removed 443 rows (71%), 179 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-858.24959 0.08047
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-168.37 -106.75 -52.96 17.97 946.03
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -866.88521 529.30355 -1.638 0.1033
currentDF[, TRAIT] -5.43009 14.44279 -0.376 0.7074
Age 0.84584 1.74525 0.485 0.6285
Gendermale -0.45921 33.91538 -0.014 0.9892
ORdate_epoch 0.07658 0.04242 1.805 0.0728 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 188.4 on 174 degrees of freedom
Multiple R-squared: 0.02322, Adjusted R-squared: 0.0007702
F-statistic: 1.034 on 4 and 174 DF, p-value: 0.391
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL5_rank
Effect size...............: -5.430086
Standard error............: 14.44279
Odds ratio (effect size)..: 0.004
Lower 95% CI..............: 0
Upper 95% CI..............: 8623750207
T-value...................: -0.375972
P-value...................: 0.7073957
R^2.......................: 0.023225
Adjusted r^2..............: 0.00077
Sample size of AE DB......: 622
Sample size of model......: 179
Missing data %............: 71.22186
- processing IL6_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-716.78685 0.06837
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-161.67 -95.79 -47.31 16.25 950.68
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -756.51217 445.29336 -1.699 0.0910 .
currentDF[, TRAIT] -7.90356 13.28158 -0.595 0.5525
Age 0.21878 1.60809 0.136 0.8919
Gendermale -2.03296 29.97184 -0.068 0.9460
ORdate_epoch 0.07049 0.03554 1.983 0.0488 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 177.6 on 184 degrees of freedom
Multiple R-squared: 0.02227, Adjusted R-squared: 0.001011
F-statistic: 1.048 on 4 and 184 DF, p-value: 0.3841
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL6_rank
Effect size...............: -7.903557
Standard error............: 13.28158
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 74651244
T-value...................: -0.595077
P-value...................: 0.5525234
R^2.......................: 0.022266
Adjusted r^2..............: 0.001011
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing IL8_rankfilter: removed 442 rows (71%), 180 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
145.01 30.18
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-188.48 -103.67 -47.33 32.35 935.66
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -530.65268 478.79178 -1.108 0.2692
currentDF[, TRAIT] 26.37301 14.29070 1.845 0.0667 .
Age 1.37192 1.63523 0.839 0.4026
Gendermale -5.45550 32.53998 -0.168 0.8670
ORdate_epoch 0.04705 0.03806 1.236 0.2180
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 183.2 on 175 degrees of freedom
Multiple R-squared: 0.03995, Adjusted R-squared: 0.01801
F-statistic: 1.821 on 4 and 175 DF, p-value: 0.1269
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL8_rank
Effect size...............: 26.37301
Standard error............: 14.2907
Odds ratio (effect size)..: 284218568638
Lower 95% CI..............: 0.195
Upper 95% CI..............: 4.150919e+23
T-value...................: 1.845466
P-value...................: 0.06665945
R^2.......................: 0.03995
Adjusted r^2..............: 0.018006
Sample size of AE DB......: 622
Sample size of model......: 180
Missing data %............: 71.06109
- processing IL9_rankfilter: removed 412 rows (66%), 210 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-696.00249 0.06765
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-213.95 -116.05 -58.99 21.66 1530.33
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -718.49466 483.27497 -1.487 0.1386
currentDF[, TRAIT] 14.63176 14.86329 0.984 0.3261
Age -0.39158 1.83372 -0.214 0.8311
Gendermale 14.03084 34.16923 0.411 0.6818
ORdate_epoch 0.07071 0.03700 1.911 0.0574 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 213.3 on 205 degrees of freedom
Multiple R-squared: 0.02205, Adjusted R-squared: 0.002967
F-statistic: 1.156 on 4 and 205 DF, p-value: 0.3317
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL9_rank
Effect size...............: 14.63176
Standard error............: 14.86329
Odds ratio (effect size)..: 2261996
Lower 95% CI..............: 0
Upper 95% CI..............: 1.014801e+19
T-value...................: 0.984422
P-value...................: 0.3260683
R^2.......................: 0.022049
Adjusted r^2..............: 0.002967
Sample size of AE DB......: 622
Sample size of model......: 210
Missing data %............: 66.23794
- processing IL10_rankfilter: removed 465 rows (75%), 157 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-977.29361 0.09042
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-182.16 -114.45 -55.79 11.15 950.36
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.072e+03 6.234e+02 -1.719 0.0876 .
currentDF[, TRAIT] 1.320e+00 1.642e+01 0.080 0.9360
Age 1.173e+00 1.953e+00 0.600 0.5491
Gendermale -4.255e+00 3.785e+01 -0.112 0.9106
ORdate_epoch 9.189e-02 4.893e-02 1.878 0.0623 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 197.2 on 152 degrees of freedom
Multiple R-squared: 0.02518, Adjusted R-squared: -0.0004686
F-statistic: 0.9817 on 4 and 152 DF, p-value: 0.4194
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL10_rank
Effect size...............: 1.320174
Standard error............: 16.41548
Odds ratio (effect size)..: 3.744
Lower 95% CI..............: 0
Upper 95% CI..............: 3.519493e+14
T-value...................: 0.080423
P-value...................: 0.936007
R^2.......................: 0.025184
Adjusted r^2..............: -0.000469
Sample size of AE DB......: 622
Sample size of model......: 157
Missing data %............: 74.75884
- processing IL12_rankfilter: removed 456 rows (73%), 166 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-882.60266 0.08273
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-166.15 -108.05 -54.61 14.28 948.35
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -906.32110 574.44389 -1.578 0.1166
currentDF[, TRAIT] -2.78021 15.34990 -0.181 0.8565
Age 0.58054 1.85981 0.312 0.7553
Gendermale -6.69042 35.82519 -0.187 0.8521
ORdate_epoch 0.08188 0.04548 1.800 0.0737 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 192.1 on 161 degrees of freedom
Multiple R-squared: 0.0221, Adjusted R-squared: -0.002193
F-statistic: 0.9097 on 4 and 161 DF, p-value: 0.4598
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL12_rank
Effect size...............: -2.780212
Standard error............: 15.3499
Odds ratio (effect size)..: 0.062
Lower 95% CI..............: 0
Upper 95% CI..............: 722215605801
T-value...................: -0.181122
P-value...................: 0.8564993
R^2.......................: 0.022102
Adjusted r^2..............: -0.002193
Sample size of AE DB......: 622
Sample size of model......: 166
Missing data %............: 73.3119
- processing IL13_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-736.7299 0.0708
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-188.20 -110.92 -63.29 14.33 1524.76
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -739.17742 467.81276 -1.580 0.1155
currentDF[, TRAIT] 7.03748 14.05520 0.501 0.6171
Age 0.10224 1.71193 0.060 0.9524
Gendermale 8.67745 32.19531 0.270 0.7878
ORdate_epoch 0.06993 0.03611 1.936 0.0541 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 210 on 225 degrees of freedom
Multiple R-squared: 0.01844, Adjusted R-squared: 0.0009909
F-statistic: 1.057 on 4 and 225 DF, p-value: 0.3788
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL13_rank
Effect size...............: 7.037476
Standard error............: 14.0552
Odds ratio (effect size)..: 1138.51
Lower 95% CI..............: 0
Upper 95% CI..............: 1.048004e+15
T-value...................: 0.500703
P-value...................: 0.6170698
R^2.......................: 0.018441
Adjusted r^2..............: 0.000991
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing IL21_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-736.7299 0.0708
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-178.63 -111.30 -63.74 14.84 1521.85
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -746.95375 467.62598 -1.597 0.1116
currentDF[, TRAIT] 3.98171 14.01324 0.284 0.7766
Age 0.04562 1.70779 0.027 0.9787
Gendermale 8.99898 32.24140 0.279 0.7804
ORdate_epoch 0.07083 0.03606 1.964 0.0507 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 210.1 on 225 degrees of freedom
Multiple R-squared: 0.0177, Adjusted R-squared: 0.0002366
F-statistic: 1.014 on 4 and 225 DF, p-value: 0.4012
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL21_rank
Effect size...............: 3.981713
Standard error............: 14.01324
Odds ratio (effect size)..: 53.609
Lower 95% CI..............: 0
Upper 95% CI..............: 4.545178e+13
T-value...................: 0.284139
P-value...................: 0.776565
R^2.......................: 0.0177
Adjusted r^2..............: 0.000237
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing INFG_rankfilter: removed 449 rows (72%), 173 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-920.70803 0.08597
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-195.67 -110.38 -56.05 25.21 956.57
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.106e+03 5.430e+02 -2.036 0.0433 *
currentDF[, TRAIT] 1.058e+01 1.566e+01 0.675 0.5005
Age 1.019e+00 1.796e+00 0.568 0.5710
Gendermale -2.742e+00 3.577e+01 -0.077 0.9390
ORdate_epoch 9.547e-02 4.272e-02 2.235 0.0267 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 190 on 168 degrees of freedom
Multiple R-squared: 0.03089, Adjusted R-squared: 0.007817
F-statistic: 1.339 on 4 and 168 DF, p-value: 0.2576
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: INFG_rank
Effect size...............: 10.57548
Standard error............: 15.66176
Odds ratio (effect size)..: 39162.83
Lower 95% CI..............: 0
Upper 95% CI..............: 8.402981e+17
T-value...................: 0.675242
P-value...................: 0.5004501
R^2.......................: 0.030891
Adjusted r^2..............: 0.007817
Sample size of AE DB......: 622
Sample size of model......: 173
Missing data %............: 72.18649
- processing TNFA_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-874.51825 0.08224
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-189.45 -111.89 -58.74 19.38 930.67
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -926.27234 575.57164 -1.609 0.1095
currentDF[, TRAIT] -11.90293 15.39735 -0.773 0.4406
Age 1.35587 1.88283 0.720 0.4725
Gendermale -1.89535 35.73818 -0.053 0.9578
ORdate_epoch 0.07911 0.04539 1.743 0.0832 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 194 on 159 degrees of freedom
Multiple R-squared: 0.02777, Adjusted R-squared: 0.003316
F-statistic: 1.136 on 4 and 159 DF, p-value: 0.3418
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: TNFA_rank
Effect size...............: -11.90293
Standard error............: 15.39735
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 86518714
T-value...................: -0.773051
P-value...................: 0.4406401
R^2.......................: 0.027775
Adjusted r^2..............: 0.003316
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing MIF_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-736.7299 0.0708
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-182.76 -109.23 -59.91 14.87 1501.78
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -939.15310 522.32673 -1.798 0.0735 .
currentDF[, TRAIT] 12.42244 15.57121 0.798 0.4258
Age 0.06449 1.70043 0.038 0.9698
Gendermale 10.15864 32.10378 0.316 0.7520
ORdate_epoch 0.08596 0.04031 2.133 0.0340 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 209.8 on 225 degrees of freedom
Multiple R-squared: 0.02012, Adjusted R-squared: 0.002699
F-statistic: 1.155 on 4 and 225 DF, p-value: 0.3316
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MIF_rank
Effect size...............: 12.42244
Standard error............: 15.57121
Odds ratio (effect size)..: 248311.7
Lower 95% CI..............: 0
Upper 95% CI..............: 4.461454e+18
T-value...................: 0.797783
P-value...................: 0.4258378
R^2.......................: 0.020119
Adjusted r^2..............: 0.002699
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing MCP1_rankfilter: removed 394 rows (63%), 228 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-721.33089 0.06964
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-184.88 -111.64 -64.80 15.03 1511.87
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -804.85997 480.08454 -1.676 0.0950 .
currentDF[, TRAIT] 8.59175 14.37254 0.598 0.5506
Age 0.22598 1.72726 0.131 0.8960
Gendermale 8.39038 32.53006 0.258 0.7967
ORdate_epoch 0.07456 0.03689 2.021 0.0445 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 210.7 on 223 degrees of freedom
Multiple R-squared: 0.01835, Adjusted R-squared: 0.0007392
F-statistic: 1.042 on 4 and 223 DF, p-value: 0.3864
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MCP1_rank
Effect size...............: 8.59175
Standard error............: 14.37254
Odds ratio (effect size)..: 5387.032
Lower 95% CI..............: 0
Upper 95% CI..............: 9.236373e+15
T-value...................: 0.597789
P-value...................: 0.5505871
R^2.......................: 0.018347
Adjusted r^2..............: 0.000739
Sample size of AE DB......: 622
Sample size of model......: 228
Missing data %............: 63.34405
- processing MIP1a_rankfilter: removed 408 rows (66%), 214 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-720.73294 0.06951
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-190.55 -110.64 -61.30 14.51 1520.83
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -707.87314 477.03085 -1.484 0.1393
currentDF[, TRAIT] 8.14128 14.57152 0.559 0.5770
Age -0.41962 1.77245 -0.237 0.8131
Gendermale 11.89573 33.91094 0.351 0.7261
ORdate_epoch 0.07003 0.03665 1.910 0.0574 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 211.9 on 209 degrees of freedom
Multiple R-squared: 0.01944, Adjusted R-squared: 0.0006685
F-statistic: 1.036 on 4 and 209 DF, p-value: 0.3898
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MIP1a_rank
Effect size...............: 8.141281
Standard error............: 14.57152
Odds ratio (effect size)..: 3433.312
Lower 95% CI..............: 0
Upper 95% CI..............: 8.694508e+15
T-value...................: 0.558712
P-value...................: 0.5769566
R^2.......................: 0.019435
Adjusted r^2..............: 0.000668
Sample size of AE DB......: 622
Sample size of model......: 214
Missing data %............: 65.59485
- processing RANTES_rankfilter: removed 396 rows (64%), 226 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-724.14436 0.06987
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-196.27 -111.21 -70.34 21.32 1510.49
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.039e+03 5.242e+02 -1.982 0.0488 *
currentDF[, TRAIT] 2.049e+01 1.575e+01 1.301 0.1947
Age 4.738e-01 1.748e+00 0.271 0.7866
Gendermale 8.943e+00 3.280e+01 0.273 0.7854
ORdate_epoch 9.181e-02 3.980e-02 2.307 0.0220 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 211.1 on 221 degrees of freedom
Multiple R-squared: 0.02427, Adjusted R-squared: 0.006613
F-statistic: 1.374 on 4 and 221 DF, p-value: 0.2437
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: RANTES_rank
Effect size...............: 20.48573
Standard error............: 15.74789
Odds ratio (effect size)..: 788571976
Lower 95% CI..............: 0
Upper 95% CI..............: 2.003143e+22
T-value...................: 1.300856
P-value...................: 0.1946624
R^2.......................: 0.024274
Adjusted r^2..............: 0.006613
Sample size of AE DB......: 622
Sample size of model......: 226
Missing data %............: 63.6656
- processing MIG_rankfilter: removed 395 rows (64%), 227 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-739.66729 0.07099
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-198.05 -112.51 -60.72 12.85 1534.45
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -653.10232 485.46691 -1.345 0.180
currentDF[, TRAIT] 11.97220 14.89654 0.804 0.422
Age 0.21629 1.73024 0.125 0.901
Gendermale 6.63324 32.60886 0.203 0.839
ORdate_epoch 0.06253 0.03787 1.651 0.100
Residual standard error: 210.6 on 222 degrees of freedom
Multiple R-squared: 0.02027, Adjusted R-squared: 0.002617
F-statistic: 1.148 on 4 and 222 DF, p-value: 0.3347
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MIG_rank
Effect size...............: 11.9722
Standard error............: 14.89654
Odds ratio (effect size)..: 158292
Lower 95% CI..............: 0
Upper 95% CI..............: 7.579701e+17
T-value...................: 0.80369
P-value...................: 0.422436
R^2.......................: 0.02027
Adjusted r^2..............: 0.002617
Sample size of AE DB......: 622
Sample size of model......: 227
Missing data %............: 63.50482
- processing IP10_rankfilter: removed 415 rows (67%), 207 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-855.34853 0.08051
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-215.74 -113.49 -58.96 9.67 1516.85
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -886.87851 496.23459 -1.787 0.0754 .
currentDF[, TRAIT] 14.14993 15.27980 0.926 0.3555
Age -0.12529 1.86540 -0.067 0.9465
Gendermale 25.15993 34.05954 0.739 0.4609
ORdate_epoch 0.08222 0.03800 2.164 0.0317 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 215.8 on 202 degrees of freedom
Multiple R-squared: 0.02872, Adjusted R-squared: 0.009491
F-statistic: 1.493 on 4 and 202 DF, p-value: 0.2055
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IP10_rank
Effect size...............: 14.14993
Standard error............: 15.2798
Odds ratio (effect size)..: 1397127
Lower 95% CI..............: 0
Upper 95% CI..............: 1.417957e+19
T-value...................: 0.926055
P-value...................: 0.3555226
R^2.......................: 0.028724
Adjusted r^2..............: 0.009491
Sample size of AE DB......: 622
Sample size of model......: 207
Missing data %............: 66.72026
- processing Eotaxin1_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-736.7299 0.0708
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-172.41 -110.21 -61.95 17.21 1510.64
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -763.69961 472.27883 -1.617 0.1073
currentDF[, TRAIT] -2.42001 14.14447 -0.171 0.8643
Age -0.01634 1.70404 -0.010 0.9924
Gendermale 10.24147 32.28179 0.317 0.7513
ORdate_epoch 0.07242 0.03647 1.986 0.0483 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 210.1 on 225 degrees of freedom
Multiple R-squared: 0.01748, Adjusted R-squared: 7.917e-06
F-statistic: 1 on 4 and 225 DF, p-value: 0.4081
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: Eotaxin1_rank
Effect size...............: -2.420015
Standard error............: 14.14447
Odds ratio (effect size)..: 0.089
Lower 95% CI..............: 0
Upper 95% CI..............: 97502826964
T-value...................: -0.171093
P-value...................: 0.8643047
R^2.......................: 0.017475
Adjusted r^2..............: 8e-06
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing TARC_rankfilter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
160.7
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-191.06 -116.19 -76.95 23.38 1528.82
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -727.73097 628.73394 -1.157 0.248
currentDF[, TRAIT] 9.51033 16.90752 0.562 0.574
Age 0.48887 1.93885 0.252 0.801
Gendermale 14.24210 36.42162 0.391 0.696
ORdate_epoch 0.06691 0.04749 1.409 0.160
Residual standard error: 221.7 on 198 degrees of freedom
Multiple R-squared: 0.01029, Adjusted R-squared: -0.009703
F-statistic: 0.5147 on 4 and 198 DF, p-value: 0.725
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: TARC_rank
Effect size...............: 9.510326
Standard error............: 16.90752
Odds ratio (effect size)..: 13498.39
Lower 95% CI..............: 0
Upper 95% CI..............: 3.328518e+18
T-value...................: 0.562491
P-value...................: 0.5744178
R^2.......................: 0.010291
Adjusted r^2..............: -0.009703
Sample size of AE DB......: 622
Sample size of model......: 203
Missing data %............: 67.36334
- processing PARC_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-996.16569 23.75445 0.09146
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-192.11 -110.59 -68.69 16.91 1483.62
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.037e+03 4.955e+02 -2.093 0.0375 *
currentDF[, TRAIT] 2.439e+01 1.474e+01 1.654 0.0995 .
Age 1.879e-01 1.694e+00 0.111 0.9118
Gendermale 1.411e+01 3.206e+01 0.440 0.6603
ORdate_epoch 9.286e-02 3.806e-02 2.440 0.0155 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 208.8 on 225 degrees of freedom
Multiple R-squared: 0.02915, Adjusted R-squared: 0.01189
F-statistic: 1.689 on 4 and 225 DF, p-value: 0.1534
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: PARC_rank
Effect size...............: 24.38967
Standard error............: 14.7444
Odds ratio (effect size)..: 39111065419
Lower 95% CI..............: 0.011
Upper 95% CI..............: 1.389897e+23
T-value...................: 1.654166
P-value...................: 0.09948824
R^2.......................: 0.029154
Adjusted r^2..............: 0.011894
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing MDC_rankfilter: removed 407 rows (65%), 215 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-709.24012 0.06856
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-195.67 -110.70 -61.38 19.68 1523.73
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -807.16415 501.83529 -1.608 0.1092
currentDF[, TRAIT] 9.78188 15.26934 0.641 0.5225
Age -0.36804 1.77425 -0.207 0.8359
Gendermale 16.04149 33.53972 0.478 0.6329
ORdate_epoch 0.07736 0.03845 2.012 0.0455 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 211.5 on 210 degrees of freedom
Multiple R-squared: 0.0197, Adjusted R-squared: 0.001031
F-statistic: 1.055 on 4 and 210 DF, p-value: 0.3798
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MDC_rank
Effect size...............: 9.78188
Standard error............: 15.26934
Odds ratio (effect size)..: 17709.92
Lower 95% CI..............: 0
Upper 95% CI..............: 1.760912e+17
T-value...................: 0.640622
P-value...................: 0.5224664
R^2.......................: 0.019703
Adjusted r^2..............: 0.001031
Sample size of AE DB......: 622
Sample size of model......: 215
Missing data %............: 65.43408
- processing OPG_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-736.7299 0.0708
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-177.42 -110.19 -62.30 15.05 1515.23
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -751.13958 467.33991 -1.607 0.1094
currentDF[, TRAIT] 2.96552 14.01556 0.212 0.8326
Age 0.04456 1.71301 0.026 0.9793
Gendermale 9.40356 32.17821 0.292 0.7704
ORdate_epoch 0.07115 0.03603 1.975 0.0495 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 210.1 on 225 degrees of freedom
Multiple R-squared: 0.01754, Adjusted R-squared: 7.678e-05
F-statistic: 1.004 on 4 and 225 DF, p-value: 0.406
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: OPG_rank
Effect size...............: 2.965519
Standard error............: 14.01556
Odds ratio (effect size)..: 19.405
Lower 95% CI..............: 0
Upper 95% CI..............: 1.65271e+13
T-value...................: 0.211588
P-value...................: 0.8326203
R^2.......................: 0.017543
Adjusted r^2..............: 7.7e-05
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing sICAM1_rankfilter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-736.7299 0.0708
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-169.49 -110.20 -62.17 17.74 1514.84
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.523e+02 4.823e+02 -1.560 0.1203
currentDF[, TRAIT] 2.714e-02 1.439e+01 0.002 0.9985
Age 1.866e-03 1.719e+00 0.001 0.9991
Gendermale 9.732e+00 3.218e+01 0.302 0.7626
ORdate_epoch 7.145e-02 3.690e-02 1.936 0.0541 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 210.1 on 225 degrees of freedom
Multiple R-squared: 0.01735, Adjusted R-squared: -0.0001222
F-statistic: 0.993 on 4 and 225 DF, p-value: 0.4122
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.027138
Standard error............: 14.3938
Odds ratio (effect size)..: 1.028
Lower 95% CI..............: 0
Upper 95% CI..............: 1.836672e+12
T-value...................: 0.001885
P-value...................: 0.9984973
R^2.......................: 0.017347
Adjusted r^2..............: -0.000122
Sample size of AE DB......: 622
Sample size of model......: 230
Missing data %............: 63.02251
- processing VEGFA_rankfilter: removed 421 rows (68%), 201 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-641.10064 0.06311
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-194.94 -109.52 -60.01 29.60 991.24
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -737.41480 534.11728 -1.381 0.169
currentDF[, TRAIT] -7.42427 15.80428 -0.470 0.639
Age 0.87765 1.75160 0.501 0.617
Gendermale -21.18834 32.67889 -0.648 0.517
ORdate_epoch 0.06729 0.04198 1.603 0.111
Residual standard error: 200.7 on 196 degrees of freedom
Multiple R-squared: 0.01929, Adjusted R-squared: -0.0007252
F-statistic: 0.9638 on 4 and 196 DF, p-value: 0.4285
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: VEGFA_rank
Effect size...............: -7.424273
Standard error............: 15.80428
Odds ratio (effect size)..: 0.001
Lower 95% CI..............: 0
Upper 95% CI..............: 16926108468
T-value...................: -0.469763
P-value...................: 0.6390463
R^2.......................: 0.019289
Adjusted r^2..............: -0.000725
Sample size of AE DB......: 622
Sample size of model......: 201
Missing data %............: 67.68489
- processing TGFB_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-911.05113 19.16758 0.08469
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-210.66 -111.76 -57.87 31.65 947.70
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -962.17837 435.55717 -2.209 0.0282 *
currentDF[, TRAIT] 19.10582 13.07276 1.461 0.1453
Age 1.26021 1.57578 0.800 0.4247
Gendermale -14.60983 29.35093 -0.498 0.6191
ORdate_epoch 0.08283 0.03364 2.462 0.0146 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 194.1 on 226 degrees of freedom
Multiple R-squared: 0.0361, Adjusted R-squared: 0.01903
F-statistic: 2.116 on 4 and 226 DF, p-value: 0.07973
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: TGFB_rank
Effect size...............: 19.10582
Standard error............: 13.07276
Odds ratio (effect size)..: 198403954
Lower 95% CI..............: 0.001
Upper 95% CI..............: 2.662604e+19
T-value...................: 1.461498
P-value...................: 0.1452677
R^2.......................: 0.036095
Adjusted r^2..............: 0.019035
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP2_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-728.15695 0.07013
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-176.98 -108.67 -59.91 21.76 1525.06
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -680.26776 474.74666 -1.433 0.1533
currentDF[, TRAIT] -7.21999 14.11863 -0.511 0.6096
Age -0.25747 1.68175 -0.153 0.8785
Gendermale 3.67236 31.78726 0.116 0.9081
ORdate_epoch 0.06748 0.03696 1.826 0.0692 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 208.1 on 226 degrees of freedom
Multiple R-squared: 0.01744, Adjusted R-squared: 5.002e-05
F-statistic: 1.003 on 4 and 226 DF, p-value: 0.4068
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MMP2_rank
Effect size...............: -7.219993
Standard error............: 14.11863
Odds ratio (effect size)..: 0.001
Lower 95% CI..............: 0
Upper 95% CI..............: 762805727
T-value...................: -0.511381
P-value...................: 0.6095838
R^2.......................: 0.01744
Adjusted r^2..............: 5e-05
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP8_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-728.15695 0.07013
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-200.97 -108.77 -61.77 21.18 1516.19
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -709.85753 465.65318 -1.524 0.1288
currentDF[, TRAIT] 17.56734 13.77906 1.275 0.2036
Age -0.14132 1.67030 -0.085 0.9326
Gendermale 1.69352 31.50543 0.054 0.9572
ORdate_epoch 0.06933 0.03641 1.904 0.0582 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 207.4 on 226 degrees of freedom
Multiple R-squared: 0.02333, Adjusted R-squared: 0.006042
F-statistic: 1.35 on 4 and 226 DF, p-value: 0.2525
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MMP8_rank
Effect size...............: 17.56734
Standard error............: 13.77906
Odds ratio (effect size)..: 42598844
Lower 95% CI..............: 0
Upper 95% CI..............: 2.282273e+19
T-value...................: 1.27493
P-value...................: 0.203643
R^2.......................: 0.023328
Adjusted r^2..............: 0.006042
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174
- processing MMP9_rankfilter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-811.68182 22.99164 0.07679
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-198.31 -108.81 -63.71 18.88 1519.29
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -810.18311 467.15318 -1.734 0.0842 .
currentDF[, TRAIT] 22.99175 13.73461 1.674 0.0955 .
Age -0.14022 1.66589 -0.084 0.9330
Gendermale 6.52146 31.22654 0.209 0.8348
ORdate_epoch 0.07705 0.03653 2.109 0.0360 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 206.9 on 226 degrees of freedom
Multiple R-squared: 0.02835, Adjusted R-squared: 0.01115
F-statistic: 1.649 on 4 and 226 DF, p-value: 0.163
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MMP9_rank
Effect size...............: 22.99175
Standard error............: 13.73461
Odds ratio (effect size)..: 9664752433
Lower 95% CI..............: 0.02
Upper 95% CI..............: 4.745957e+21
T-value...................: 1.674001
P-value...................: 0.09551367
R^2.......................: 0.028351
Adjusted r^2..............: 0.011154
Sample size of AE DB......: 622
Sample size of model......: 231
Missing data %............: 62.86174 Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AERNASE.clin.Con.Uni.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
rowNmes = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...Model 2
In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of plaque cytokines as a function of plaque PCSK9 levels.
Running linear regression...for (protein in 1:length(TRAITS.TARGET.RANK)) {
PROTEIN = TRAITS.TARGET.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(proteins_of_interest_rank)) {
TRAIT = proteins_of_interest_rank[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AERNASE.clin %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
# fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year +
# Hypertension.composite + DiabetesStatus + SmokerStatus +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# MedHx_CVD + stenose,
# data = currentDF)
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of CXCL10.
- processing IL2_rankfilter: removed 459 rows (74%), 163 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + BMI, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch BMI
-26.965065 0.002013 0.150337
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.8102 -2.1998 -0.8173 0.5525 29.0543
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -26.613781 14.891061 -1.787 0.0760 .
currentDF[, TRAIT] 0.233249 0.384840 0.606 0.5454
Age -0.045868 0.051403 -0.892 0.3737
Gendermale 0.898614 0.924728 0.972 0.3328
ORdate_epoch 0.002064 0.001062 1.943 0.0539 .
Hypertension.compositeyes -0.361148 1.203986 -0.300 0.7646
DiabetesStatusDiabetes -0.418556 1.008852 -0.415 0.6788
SmokerStatusEx-smoker 1.109738 0.864598 1.284 0.2013
SmokerStatusNever smoked 0.829336 1.177200 0.704 0.4822
Med.Statin.LLDyes -0.009352 0.849342 -0.011 0.9912
Med.all.antiplateletyes 0.839985 1.345878 0.624 0.5335
GFR_MDRD -0.024966 0.022087 -1.130 0.2602
BMI 0.135437 0.114595 1.182 0.2392
MedHx_CVDNo -0.825816 0.803556 -1.028 0.3058
stenose70-90% 3.634166 3.438166 1.057 0.2923
stenose90-99% 2.539691 3.416149 0.743 0.4584
stenose100% (Occlusion) 3.495347 4.975688 0.702 0.4835
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.586 on 146 degrees of freedom
Multiple R-squared: 0.0945, Adjusted R-squared: -0.004732
F-statistic: 0.9523 on 16 and 146 DF, p-value: 0.5118
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL2_rank
Effect size...............: 0.233249
Standard error............: 0.38484
Odds ratio (effect size)..: 1.263
Lower 95% CI..............: 0.594
Upper 95% CI..............: 2.685
T-value...................: 0.606094
P-value...................: 0.5453937
R^2.......................: 0.094501
Adjusted r^2..............: -0.004732
Sample size of AE DB......: 622
Sample size of model......: 163
Missing data %............: 73.79421
- processing IL4_rankfilter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch GFR_MDRD
-28.954036 0.002731 -0.037274
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.4544 -2.3573 -0.8411 0.5571 28.7159
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -30.671082 17.575951 -1.745 0.0834 .
currentDF[, TRAIT] 0.236251 0.453776 0.521 0.6035
Age -0.057861 0.059475 -0.973 0.3325
Gendermale 1.377685 1.062388 1.297 0.1970
ORdate_epoch 0.002784 0.001261 2.208 0.0290 *
Hypertension.compositeyes -0.330136 1.395449 -0.237 0.8134
DiabetesStatusDiabetes -0.956007 1.160746 -0.824 0.4117
SmokerStatusEx-smoker 1.036455 0.968320 1.070 0.2865
SmokerStatusNever smoked 1.053106 1.375967 0.765 0.4455
Med.Statin.LLDyes -0.235247 1.015624 -0.232 0.8172
Med.all.antiplateletyes 0.869773 1.524430 0.571 0.5693
GFR_MDRD -0.048440 0.027695 -1.749 0.0827 .
BMI 0.057516 0.138698 0.415 0.6791
MedHx_CVDNo -0.457341 0.924079 -0.495 0.6215
stenose70-90% 3.620743 3.759361 0.963 0.3373
stenose90-99% 2.017297 3.729322 0.541 0.5895
stenose100% (Occlusion) 2.602158 5.449010 0.478 0.6338
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.953 on 128 degrees of freedom
Multiple R-squared: 0.1154, Adjusted R-squared: 0.004871
F-statistic: 1.044 on 16 and 128 DF, p-value: 0.4158
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL4_rank
Effect size...............: 0.236251
Standard error............: 0.453776
Odds ratio (effect size)..: 1.266
Lower 95% CI..............: 0.52
Upper 95% CI..............: 3.082
T-value...................: 0.520633
P-value...................: 0.6035218
R^2.......................: 0.115441
Adjusted r^2..............: 0.004871
Sample size of AE DB......: 622
Sample size of model......: 145
Missing data %............: 76.6881
- processing IL5_rankfilter: removed 464 rows (75%), 158 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + GFR_MDRD +
BMI, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch GFR_MDRD BMI
-30.628312 0.002492 -0.032294 0.156786
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.1546 -2.4853 -1.0374 0.4801 28.7209
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -31.159925 16.068294 -1.939 0.0545 .
currentDF[, TRAIT] 0.255883 0.409682 0.625 0.5333
Age -0.033012 0.056703 -0.582 0.5614
Gendermale 1.455574 0.988904 1.472 0.1433
ORdate_epoch 0.002418 0.001172 2.063 0.0410 *
Hypertension.compositeyes -0.481366 1.310381 -0.367 0.7139
DiabetesStatusDiabetes -0.742177 1.065178 -0.697 0.4871
SmokerStatusEx-smoker 0.694868 0.900839 0.771 0.4418
SmokerStatusNever smoked 0.868728 1.300973 0.668 0.5054
Med.Statin.LLDyes 0.278503 0.926781 0.301 0.7642
Med.all.antiplateletyes 0.967342 1.435599 0.674 0.5015
GFR_MDRD -0.037432 0.024618 -1.521 0.1306
BMI 0.159403 0.122213 1.304 0.1943
MedHx_CVDNo -0.548693 0.874178 -0.628 0.5312
stenose70-90% 2.907378 3.025792 0.961 0.3383
stenose90-99% 1.689514 2.988847 0.565 0.5728
stenose100% (Occlusion) 3.185808 4.830442 0.660 0.5106
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.84 on 141 degrees of freedom
Multiple R-squared: 0.1006, Adjusted R-squared: -0.001499
F-statistic: 0.9853 on 16 and 141 DF, p-value: 0.4761
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL5_rank
Effect size...............: 0.255883
Standard error............: 0.409682
Odds ratio (effect size)..: 1.292
Lower 95% CI..............: 0.579
Upper 95% CI..............: 2.883
T-value...................: 0.624589
P-value...................: 0.5332504
R^2.......................: 0.100565
Adjusted r^2..............: -0.001499
Sample size of AE DB......: 622
Sample size of model......: 158
Missing data %............: 74.59807
- processing IL6_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-20.823185 0.001846
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.6223 -2.3248 -0.9519 0.2217 29.0233
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -24.070206 16.225130 -1.484 0.140
currentDF[, TRAIT] 0.180702 0.388833 0.465 0.643
Age -0.033338 0.056834 -0.587 0.558
Gendermale 1.152792 0.935371 1.232 0.220
ORdate_epoch 0.001741 0.001081 1.611 0.109
Hypertension.compositeyes -0.212134 1.249760 -0.170 0.865
DiabetesStatusDiabetes -1.051638 1.045672 -1.006 0.316
SmokerStatusEx-smoker 0.989081 0.897101 1.103 0.272
SmokerStatusNever smoked 0.305141 1.244661 0.245 0.807
Med.Statin.LLDyes 0.124275 0.884217 0.141 0.888
Med.all.antiplateletyes 0.905033 1.433893 0.631 0.529
GFR_MDRD -0.024336 0.024167 -1.007 0.316
BMI 0.094031 0.103657 0.907 0.366
MedHx_CVDNo -0.381180 0.843062 -0.452 0.652
stenose50-70% 2.243442 5.767757 0.389 0.698
stenose70-90% 4.847430 5.011080 0.967 0.335
stenose90-99% 3.928935 5.001896 0.785 0.433
stenose100% (Occlusion) 4.403998 6.314089 0.697 0.487
Residual standard error: 4.809 on 146 degrees of freedom
Multiple R-squared: 0.07929, Adjusted R-squared: -0.02791
F-statistic: 0.7396 on 17 and 146 DF, p-value: 0.758
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL6_rank
Effect size...............: 0.180702
Standard error............: 0.388833
Odds ratio (effect size)..: 1.198
Lower 95% CI..............: 0.559
Upper 95% CI..............: 2.567
T-value...................: 0.464729
P-value...................: 0.6428177
R^2.......................: 0.079292
Adjusted r^2..............: -0.027914
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing IL8_rankfilter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch GFR_MDRD
-17.146035 0.001704 -0.028101
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.5794 -2.1978 -0.9879 0.1948 28.8071
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -25.372884 16.798184 -1.510 0.1332
currentDF[, TRAIT] 0.086622 0.405964 0.213 0.8314
Age -0.070141 0.054942 -1.277 0.2039
Gendermale 0.624371 0.981022 0.636 0.5256
ORdate_epoch 0.002072 0.001118 1.853 0.0660 .
Hypertension.compositeyes -0.267889 1.216886 -0.220 0.8261
DiabetesStatusDiabetes -1.172524 1.030836 -1.137 0.2574
SmokerStatusEx-smoker 0.649011 0.920980 0.705 0.4822
SmokerStatusNever smoked -0.214129 1.325466 -0.162 0.8719
Med.Statin.LLDyes -0.316973 0.881968 -0.359 0.7199
Med.all.antiplateletyes 1.321010 1.358834 0.972 0.3327
GFR_MDRD -0.038620 0.021732 -1.777 0.0778 .
BMI 0.113641 0.103837 1.094 0.2757
MedHx_CVDNo -0.389126 0.859707 -0.453 0.6515
stenose50-70% 2.958100 5.629879 0.525 0.6001
stenose70-90% 5.005964 4.916747 1.018 0.3104
stenose90-99% 4.920146 4.898863 1.004 0.3170
stenose100% (Occlusion) 6.072214 6.993967 0.868 0.3868
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.7 on 136 degrees of freedom
Multiple R-squared: 0.08371, Adjusted R-squared: -0.03083
F-statistic: 0.7308 on 17 and 136 DF, p-value: 0.7669
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL8_rank
Effect size...............: 0.086622
Standard error............: 0.405964
Odds ratio (effect size)..: 1.09
Lower 95% CI..............: 0.492
Upper 95% CI..............: 2.416
T-value...................: 0.213373
P-value...................: 0.8313554
R^2.......................: 0.083707
Adjusted r^2..............: -0.03083
Sample size of AE DB......: 622
Sample size of model......: 154
Missing data %............: 75.24116
- processing IL9_rankfilter: removed 436 rows (70%), 186 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Hypertension.composite,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Hypertension.compositeyes
-16.600936 0.001387 1.535080
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.122 -2.296 -1.159 0.441 29.309
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.009e+01 1.385e+01 -1.450 0.1489
currentDF[, TRAIT] 4.150e-01 3.488e-01 1.190 0.2358
Age -4.446e-02 5.076e-02 -0.876 0.3824
Gendermale 5.943e-01 8.415e-01 0.706 0.4810
ORdate_epoch 1.536e-03 9.074e-04 1.693 0.0923 .
Hypertension.compositeyes 1.260e+00 1.136e+00 1.109 0.2691
DiabetesStatusDiabetes -1.257e+00 9.405e-01 -1.336 0.1833
SmokerStatusEx-smoker 5.005e-01 8.221e-01 0.609 0.5435
SmokerStatusNever smoked -3.921e-01 1.047e+00 -0.375 0.7084
Med.Statin.LLDyes 5.199e-01 8.313e-01 0.625 0.5325
Med.all.antiplateletyes 7.056e-01 1.374e+00 0.513 0.6083
GFR_MDRD -1.496e-02 1.912e-02 -0.782 0.4351
BMI 2.305e-02 9.260e-02 0.249 0.8037
MedHx_CVDNo 1.917e-01 7.598e-01 0.252 0.8011
stenose50-70% 1.406e+00 5.914e+00 0.238 0.8123
stenose70-90% 4.108e+00 4.792e+00 0.857 0.3926
stenose90-99% 3.665e+00 4.772e+00 0.768 0.4435
stenose100% (Occlusion) 4.244e+00 6.070e+00 0.699 0.4854
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.635 on 168 degrees of freedom
Multiple R-squared: 0.07148, Adjusted R-squared: -0.02248
F-statistic: 0.7608 on 17 and 168 DF, p-value: 0.7357
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL9_rank
Effect size...............: 0.415021
Standard error............: 0.348802
Odds ratio (effect size)..: 1.514
Lower 95% CI..............: 0.764
Upper 95% CI..............: 3
T-value...................: 1.189849
P-value...................: 0.2357847
R^2.......................: 0.071479
Adjusted r^2..............: -0.022479
Sample size of AE DB......: 622
Sample size of model......: 186
Missing data %............: 70.09646
- processing IL10_rankfilter: removed 483 rows (78%), 139 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + SmokerStatus,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch SmokerStatusEx-smoker SmokerStatusNever smoked
-25.428594 0.002138 1.830375 1.457255
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.3385 -2.1954 -0.8307 0.8579 28.2056
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -32.444912 17.956566 -1.807 0.0732 .
currentDF[, TRAIT] 0.665837 0.435987 1.527 0.1293
Age -0.042987 0.058584 -0.734 0.4645
Gendermale 1.089040 1.032505 1.055 0.2936
ORdate_epoch 0.002616 0.001256 2.082 0.0394 *
Hypertension.compositeyes -0.832566 1.365092 -0.610 0.5431
DiabetesStatusDiabetes -0.713962 1.127687 -0.633 0.5278
SmokerStatusEx-smoker 1.376722 0.953165 1.444 0.1512
SmokerStatusNever smoked 1.625991 1.326483 1.226 0.2226
Med.Statin.LLDyes 0.074253 0.950585 0.078 0.9379
Med.all.antiplateletyes 0.849033 1.477770 0.575 0.5667
GFR_MDRD -0.041386 0.027093 -1.528 0.1292
BMI 0.136605 0.133844 1.021 0.3095
MedHx_CVDNo -0.983711 0.935104 -1.052 0.2949
stenose70-90% 3.878432 3.606751 1.075 0.2844
stenose90-99% 2.546335 3.577950 0.712 0.4780
stenose100% (Occlusion) 4.104626 5.233282 0.784 0.4344
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.738 on 122 degrees of freedom
Multiple R-squared: 0.133, Adjusted R-squared: 0.01926
F-statistic: 1.169 on 16 and 122 DF, p-value: 0.302
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL10_rank
Effect size...............: 0.665837
Standard error............: 0.435987
Odds ratio (effect size)..: 1.946
Lower 95% CI..............: 0.828
Upper 95% CI..............: 4.574
T-value...................: 1.527194
P-value...................: 0.1293012
R^2.......................: 0.132971
Adjusted r^2..............: 0.019263
Sample size of AE DB......: 622
Sample size of model......: 139
Missing data %............: 77.65273
- processing IL12_rankfilter: removed 476 rows (77%), 146 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-29.230557 0.002538
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.2329 -2.4963 -0.8780 0.4558 28.4544
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -33.228837 18.398873 -1.806 0.0732 .
currentDF[, TRAIT] 0.410471 0.447604 0.917 0.3608
Age -0.038350 0.061749 -0.621 0.5357
Gendermale 1.345566 1.058616 1.271 0.2060
ORdate_epoch 0.002689 0.001281 2.099 0.0378 *
Hypertension.compositeyes -0.784181 1.439824 -0.545 0.5869
DiabetesStatusDiabetes -0.833964 1.147111 -0.727 0.4685
SmokerStatusEx-smoker 0.873933 1.018028 0.858 0.3922
SmokerStatusNever smoked 0.821565 1.350824 0.608 0.5441
Med.Statin.LLDyes 0.146453 1.013290 0.145 0.8853
Med.all.antiplateletyes 1.311922 1.540053 0.852 0.3959
GFR_MDRD -0.037668 0.027256 -1.382 0.1694
BMI 0.096007 0.137916 0.696 0.4876
MedHx_CVDNo -0.576829 0.929073 -0.621 0.5358
stenose70-90% 3.816594 3.771131 1.012 0.3134
stenose90-99% 2.610975 3.756064 0.695 0.4882
stenose100% (Occlusion) 4.372178 6.607627 0.662 0.5094
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.98 on 129 degrees of freedom
Multiple R-squared: 0.1007, Adjusted R-squared: -0.01089
F-statistic: 0.9024 on 16 and 129 DF, p-value: 0.5679
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL12_rank
Effect size...............: 0.410471
Standard error............: 0.447604
Odds ratio (effect size)..: 1.508
Lower 95% CI..............: 0.627
Upper 95% CI..............: 3.625
T-value...................: 0.917041
P-value...................: 0.3608322
R^2.......................: 0.100656
Adjusted r^2..............: -0.01089
Sample size of AE DB......: 622
Sample size of model......: 146
Missing data %............: 76.52733
- processing IL13_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-15.817355 0.001434
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.5171 -2.2312 -1.1643 0.2673 29.5819
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.072e+01 1.361e+01 -1.523 0.1296
currentDF[, TRAIT] 3.556e-01 3.505e-01 1.015 0.3116
Age -2.353e-02 4.839e-02 -0.486 0.6273
Gendermale 5.905e-01 8.116e-01 0.728 0.4678
ORdate_epoch 1.495e-03 9.026e-04 1.656 0.0993 .
Hypertension.compositeyes 3.562e-01 1.060e+00 0.336 0.7372
DiabetesStatusDiabetes -1.114e+00 8.774e-01 -1.270 0.2058
SmokerStatusEx-smoker 5.762e-01 7.916e-01 0.728 0.4676
SmokerStatusNever smoked -3.711e-01 1.036e+00 -0.358 0.7206
Med.Statin.LLDyes 1.709e-01 7.870e-01 0.217 0.8283
Med.all.antiplateletyes 7.402e-01 1.225e+00 0.604 0.5466
GFR_MDRD -9.251e-03 1.876e-02 -0.493 0.6225
BMI 5.320e-02 8.781e-02 0.606 0.5453
MedHx_CVDNo -5.676e-04 7.174e-01 -0.001 0.9994
stenose50-70% 1.230e+00 5.561e+00 0.221 0.8253
stenose70-90% 3.826e+00 4.815e+00 0.795 0.4278
stenose90-99% 3.211e+00 4.809e+00 0.668 0.5051
stenose100% (Occlusion) 3.658e+00 6.029e+00 0.607 0.5448
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.658 on 184 degrees of freedom
Multiple R-squared: 0.05661, Adjusted R-squared: -0.03055
F-statistic: 0.6495 on 17 and 184 DF, p-value: 0.8485
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL13_rank
Effect size...............: 0.35565
Standard error............: 0.350519
Odds ratio (effect size)..: 1.427
Lower 95% CI..............: 0.718
Upper 95% CI..............: 2.837
T-value...................: 1.014639
P-value...................: 0.3116104
R^2.......................: 0.056609
Adjusted r^2..............: -0.030552
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing IL21_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-15.817355 0.001434
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.6783 -2.3315 -1.1407 0.1965 29.5386
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.109e+01 1.364e+01 -1.546 0.1237
currentDF[, TRAIT] 1.937e-01 3.484e-01 0.556 0.5789
Age -2.941e-02 4.805e-02 -0.612 0.5413
Gendermale 6.190e-01 8.147e-01 0.760 0.4483
ORdate_epoch 1.549e-03 9.027e-04 1.716 0.0879 .
Hypertension.compositeyes 2.924e-01 1.059e+00 0.276 0.7828
DiabetesStatusDiabetes -1.159e+00 8.778e-01 -1.320 0.1884
SmokerStatusEx-smoker 6.529e-01 7.884e-01 0.828 0.4087
SmokerStatusNever smoked -2.629e-01 1.033e+00 -0.254 0.7995
Med.Statin.LLDyes 1.914e-01 7.898e-01 0.242 0.8088
Med.all.antiplateletyes 7.499e-01 1.229e+00 0.610 0.5424
GFR_MDRD -9.941e-03 1.878e-02 -0.529 0.5973
BMI 5.013e-02 8.791e-02 0.570 0.5692
MedHx_CVDNo -2.521e-02 7.184e-01 -0.035 0.9720
stenose50-70% 1.514e+00 5.566e+00 0.272 0.7859
stenose70-90% 4.004e+00 4.822e+00 0.830 0.4074
stenose90-99% 3.448e+00 4.816e+00 0.716 0.4750
stenose100% (Occlusion) 3.588e+00 6.042e+00 0.594 0.5533
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.667 on 184 degrees of freedom
Multiple R-squared: 0.05292, Adjusted R-squared: -0.03458
F-statistic: 0.6048 on 17 and 184 DF, p-value: 0.8857
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IL21_rank
Effect size...............: 0.193728
Standard error............: 0.348445
Odds ratio (effect size)..: 1.214
Lower 95% CI..............: 0.613
Upper 95% CI..............: 2.403
T-value...................: 0.555978
P-value...................: 0.5789017
R^2.......................: 0.052921
Adjusted r^2..............: -0.03458
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing INFG_rankfilter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-24.330215 0.002138
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.9977 -2.2197 -0.9322 0.6158 28.3068
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -34.240174 17.435747 -1.964 0.0516 .
currentDF[, TRAIT] 0.730254 0.451442 1.618 0.1081
Age -0.040581 0.057470 -0.706 0.4813
Gendermale 1.557620 1.048927 1.485 0.1399
ORdate_epoch 0.002724 0.001180 2.309 0.0224 *
Hypertension.compositeyes -0.652113 1.389311 -0.469 0.6396
DiabetesStatusDiabetes -1.190103 1.055012 -1.128 0.2613
SmokerStatusEx-smoker 0.927986 0.950216 0.977 0.3305
SmokerStatusNever smoked 0.641096 1.291757 0.496 0.6205
Med.Statin.LLDyes 0.217899 0.939281 0.232 0.8169
Med.all.antiplateletyes 1.465532 1.376741 1.064 0.2890
GFR_MDRD -0.033005 0.023221 -1.421 0.1575
BMI 0.055095 0.107280 0.514 0.6084
MedHx_CVDNo 0.026523 0.905600 0.029 0.9767
stenose50-70% 1.740992 5.813769 0.299 0.7650
stenose70-90% 4.648722 5.072055 0.917 0.3610
stenose90-99% 3.487388 5.043908 0.691 0.4905
stenose100% (Occlusion) 4.691270 7.168182 0.654 0.5139
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.839 on 136 degrees of freedom
Multiple R-squared: 0.1111, Adjusted R-squared: 4.047e-05
F-statistic: 1 on 17 and 136 DF, p-value: 0.4619
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: INFG_rank
Effect size...............: 0.730254
Standard error............: 0.451442
Odds ratio (effect size)..: 2.076
Lower 95% CI..............: 0.857
Upper 95% CI..............: 5.028
T-value...................: 1.617602
P-value...................: 0.1080654
R^2.......................: 0.111147
Adjusted r^2..............: 4e-05
Sample size of AE DB......: 622
Sample size of model......: 154
Missing data %............: 75.24116
- processing TNFA_rankfilter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch GFR_MDRD
-20.810847 0.002042 -0.033496
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.495 -2.473 -1.039 0.399 28.581
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -26.675872 17.880816 -1.492 0.1382
currentDF[, TRAIT] 0.330955 0.449663 0.736 0.4631
Age -0.038693 0.060744 -0.637 0.5253
Gendermale 1.429970 1.061071 1.348 0.1801
ORdate_epoch 0.002138 0.001246 1.716 0.0886 .
Hypertension.compositeyes -0.689387 1.422087 -0.485 0.6287
DiabetesStatusDiabetes -1.030423 1.148280 -0.897 0.3712
SmokerStatusEx-smoker 1.025142 0.989971 1.036 0.3024
SmokerStatusNever smoked 0.769191 1.371219 0.561 0.5758
Med.Statin.LLDyes 0.066400 1.019920 0.065 0.9482
Med.all.antiplateletyes 1.331928 1.530744 0.870 0.3859
GFR_MDRD -0.037587 0.026376 -1.425 0.1566
BMI 0.107176 0.130626 0.820 0.4135
MedHx_CVDNo -0.296667 0.960024 -0.309 0.7578
stenose70-90% 3.225127 3.792502 0.850 0.3967
stenose90-99% 2.432635 3.753984 0.648 0.5181
stenose100% (Occlusion) 4.204330 6.614458 0.636 0.5262
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.014 on 128 degrees of freedom
Multiple R-squared: 0.09119, Adjusted R-squared: -0.02241
F-statistic: 0.8027 on 16 and 128 DF, p-value: 0.6804
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: TNFA_rank
Effect size...............: 0.330955
Standard error............: 0.449663
Odds ratio (effect size)..: 1.392
Lower 95% CI..............: 0.577
Upper 95% CI..............: 3.361
T-value...................: 0.736006
P-value...................: 0.4630739
R^2.......................: 0.09119
Adjusted r^2..............: -0.022412
Sample size of AE DB......: 622
Sample size of model......: 145
Missing data %............: 76.6881
- processing MIF_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-15.817355 0.001434
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.796 -2.374 -1.067 0.196 29.685
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -20.503691 14.763319 -1.389 0.167
currentDF[, TRAIT] -0.078909 0.404168 -0.195 0.845
Age -0.034423 0.047640 -0.723 0.471
Gendermale 0.678848 0.810849 0.837 0.404
ORdate_epoch 0.001502 0.001010 1.486 0.139
Hypertension.compositeyes 0.240569 1.066024 0.226 0.822
DiabetesStatusDiabetes -1.206368 0.877977 -1.374 0.171
SmokerStatusEx-smoker 0.691972 0.785817 0.881 0.380
SmokerStatusNever smoked -0.141410 1.022738 -0.138 0.890
Med.Statin.LLDyes 0.241017 0.787612 0.306 0.760
Med.all.antiplateletyes 0.803801 1.232477 0.652 0.515
GFR_MDRD -0.010619 0.019073 -0.557 0.578
BMI 0.049580 0.087991 0.563 0.574
MedHx_CVDNo -0.053994 0.719365 -0.075 0.940
stenose50-70% 1.848423 5.565207 0.332 0.740
stenose70-90% 4.260740 4.833270 0.882 0.379
stenose90-99% 3.800655 4.823301 0.788 0.432
stenose100% (Occlusion) 3.705747 6.052527 0.612 0.541
Residual standard error: 4.67 on 184 degrees of freedom
Multiple R-squared: 0.05153, Adjusted R-squared: -0.0361
F-statistic: 0.588 on 17 and 184 DF, p-value: 0.8983
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MIF_rank
Effect size...............: -0.078909
Standard error............: 0.404168
Odds ratio (effect size)..: 0.924
Lower 95% CI..............: 0.418
Upper 95% CI..............: 2.041
T-value...................: -0.195239
P-value...................: 0.8454212
R^2.......................: 0.051527
Adjusted r^2..............: -0.036104
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MCP1_rankfilter: removed 422 rows (68%), 200 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-15.463601 0.001407
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.0703 -2.3818 -1.1334 0.1454 29.5535
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.038e+01 1.409e+01 -1.447 0.150
currentDF[, TRAIT] -1.439e-01 3.467e-01 -0.415 0.679
Age -3.483e-02 4.840e-02 -0.720 0.473
Gendermale 7.015e-01 8.254e-01 0.850 0.397
ORdate_epoch 1.504e-03 9.321e-04 1.613 0.108
Hypertension.compositeyes 2.773e-01 1.073e+00 0.258 0.796
DiabetesStatusDiabetes -1.248e+00 8.855e-01 -1.409 0.160
SmokerStatusEx-smoker 7.275e-01 7.902e-01 0.921 0.358
SmokerStatusNever smoked -1.203e-01 1.035e+00 -0.116 0.908
Med.Statin.LLDyes 2.017e-01 7.955e-01 0.254 0.800
Med.all.antiplateletyes 6.806e-01 1.311e+00 0.519 0.604
GFR_MDRD -9.819e-03 1.898e-02 -0.517 0.606
BMI 4.814e-02 8.984e-02 0.536 0.593
MedHx_CVDNo -6.055e-02 7.267e-01 -0.083 0.934
stenose50-70% 1.830e+00 5.579e+00 0.328 0.743
stenose70-90% 4.234e+00 4.840e+00 0.875 0.383
stenose90-99% 3.752e+00 4.823e+00 0.778 0.438
stenose100% (Occlusion) 3.476e+00 6.094e+00 0.570 0.569
Residual standard error: 4.692 on 182 degrees of freedom
Multiple R-squared: 0.05082, Adjusted R-squared: -0.03784
F-statistic: 0.5732 on 17 and 182 DF, p-value: 0.9086
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MCP1_rank
Effect size...............: -0.143879
Standard error............: 0.346731
Odds ratio (effect size)..: 0.866
Lower 95% CI..............: 0.439
Upper 95% CI..............: 1.709
T-value...................: -0.414958
P-value...................: 0.6786616
R^2.......................: 0.050816
Adjusted r^2..............: -0.037844
Sample size of AE DB......: 622
Sample size of model......: 200
Missing data %............: 67.84566
- processing MIP1a_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-14.387348 0.001324
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.1664 -2.4323 -1.0745 0.1447 29.3884
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.002e+01 1.421e+01 -1.409 0.161
currentDF[, TRAIT] 8.331e-02 3.648e-01 0.228 0.820
Age -3.893e-02 5.057e-02 -0.770 0.442
Gendermale 7.851e-01 8.737e-01 0.899 0.370
ORdate_epoch 1.470e-03 9.377e-04 1.567 0.119
Hypertension.compositeyes 4.160e-01 1.145e+00 0.363 0.717
DiabetesStatusDiabetes -1.459e+00 9.436e-01 -1.546 0.124
SmokerStatusEx-smoker 7.577e-01 8.456e-01 0.896 0.371
SmokerStatusNever smoked -2.148e-01 1.086e+00 -0.198 0.844
Med.Statin.LLDyes 2.863e-01 8.440e-01 0.339 0.735
Med.all.antiplateletyes 9.359e-01 1.417e+00 0.661 0.510
GFR_MDRD -1.263e-02 1.973e-02 -0.640 0.523
BMI 5.272e-02 9.342e-02 0.564 0.573
MedHx_CVDNo 3.679e-02 7.834e-01 0.047 0.963
stenose50-70% 1.832e+00 6.144e+00 0.298 0.766
stenose70-90% 4.302e+00 4.975e+00 0.865 0.388
stenose90-99% 3.627e+00 4.967e+00 0.730 0.466
stenose100% (Occlusion) 3.789e+00 6.265e+00 0.605 0.546
Residual standard error: 4.797 on 171 degrees of freedom
Multiple R-squared: 0.05779, Adjusted R-squared: -0.03588
F-statistic: 0.617 on 17 and 171 DF, p-value: 0.8756
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.083311
Standard error............: 0.364823
Odds ratio (effect size)..: 1.087
Lower 95% CI..............: 0.532
Upper 95% CI..............: 2.222
T-value...................: 0.22836
P-value...................: 0.8196395
R^2.......................: 0.057793
Adjusted r^2..............: -0.035877
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing RANTES_rankfilter: removed 424 rows (68%), 198 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-15.420322 0.001404
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.0265 -2.3826 -1.0165 0.1318 29.6057
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.423e+01 1.466e+01 -1.652 0.1002
currentDF[, TRAIT] 1.693e-01 3.847e-01 0.440 0.6604
Age -2.847e-02 4.897e-02 -0.581 0.5617
Gendermale 7.307e-01 8.378e-01 0.872 0.3842
ORdate_epoch 1.744e-03 9.886e-04 1.764 0.0794 .
Hypertension.compositeyes 3.848e-01 1.111e+00 0.346 0.7295
DiabetesStatusDiabetes -1.141e+00 9.058e-01 -1.259 0.2096
SmokerStatusEx-smoker 6.926e-01 8.082e-01 0.857 0.3926
SmokerStatusNever smoked -2.362e-01 1.045e+00 -0.226 0.8215
Med.Statin.LLDyes 2.816e-01 8.018e-01 0.351 0.7258
Med.all.antiplateletyes 9.296e-01 1.308e+00 0.711 0.4782
GFR_MDRD -9.678e-03 1.911e-02 -0.506 0.6132
BMI 5.102e-02 9.028e-02 0.565 0.5727
MedHx_CVDNo 2.006e-02 7.497e-01 0.027 0.9787
stenose50-70% 1.881e+00 5.982e+00 0.314 0.7535
stenose70-90% 4.145e+00 4.880e+00 0.849 0.3967
stenose90-99% 3.606e+00 4.855e+00 0.743 0.4586
stenose100% (Occlusion) 3.724e+00 6.154e+00 0.605 0.5458
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.711 on 180 degrees of freedom
Multiple R-squared: 0.05263, Adjusted R-squared: -0.03685
F-statistic: 0.5882 on 17 and 180 DF, p-value: 0.898
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: RANTES_rank
Effect size...............: 0.16933
Standard error............: 0.384733
Odds ratio (effect size)..: 1.185
Lower 95% CI..............: 0.557
Upper 95% CI..............: 2.518
T-value...................: 0.440123
P-value...................: 0.660376
R^2.......................: 0.052628
Adjusted r^2..............: -0.036846
Sample size of AE DB......: 622
Sample size of model......: 198
Missing data %............: 68.1672
- processing MIG_rankfilter: removed 423 rows (68%), 199 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-15.827596 0.001435
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.6510 -2.3429 -1.1303 0.1877 29.2779
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.866e+01 1.422e+01 -1.313 0.191
currentDF[, TRAIT] 3.685e-01 3.824e-01 0.964 0.337
Age -2.516e-02 4.874e-02 -0.516 0.606
Gendermale 6.249e-01 8.273e-01 0.755 0.451
ORdate_epoch 1.330e-03 9.562e-04 1.391 0.166
Hypertension.compositeyes 3.531e-01 1.097e+00 0.322 0.748
DiabetesStatusDiabetes -1.190e+00 8.972e-01 -1.326 0.186
SmokerStatusEx-smoker 5.827e-01 8.026e-01 0.726 0.469
SmokerStatusNever smoked -3.238e-01 1.050e+00 -0.308 0.758
Med.Statin.LLDyes 2.407e-01 8.027e-01 0.300 0.765
Med.all.antiplateletyes 8.730e-01 1.262e+00 0.692 0.490
GFR_MDRD -1.202e-02 1.896e-02 -0.634 0.527
BMI 5.085e-02 8.853e-02 0.574 0.566
MedHx_CVDNo -8.141e-02 7.348e-01 -0.111 0.912
stenose50-70% 1.550e+00 5.993e+00 0.259 0.796
stenose70-90% 4.040e+00 4.847e+00 0.834 0.406
stenose90-99% 3.395e+00 4.836e+00 0.702 0.484
stenose100% (Occlusion) 3.784e+00 6.090e+00 0.621 0.535
Residual standard error: 4.689 on 181 degrees of freedom
Multiple R-squared: 0.0577, Adjusted R-squared: -0.0308
F-statistic: 0.652 on 17 and 181 DF, p-value: 0.8461
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MIG_rank
Effect size...............: 0.368488
Standard error............: 0.382391
Odds ratio (effect size)..: 1.446
Lower 95% CI..............: 0.683
Upper 95% CI..............: 3.059
T-value...................: 0.963642
P-value...................: 0.3365111
R^2.......................: 0.057702
Adjusted r^2..............: -0.030802
Sample size of AE DB......: 622
Sample size of model......: 199
Missing data %............: 68.00643
- processing IP10_rankfilter: removed 439 rows (71%), 183 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-15.749874 0.699569 0.001434
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.769 -2.434 -1.010 0.481 29.131
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.305e+01 1.440e+01 -1.600 0.1114
currentDF[, TRAIT] 6.524e-01 3.856e-01 1.692 0.0926 .
Age -2.311e-02 5.263e-02 -0.439 0.6612
Gendermale 9.332e-01 8.639e-01 1.080 0.2816
ORdate_epoch 1.656e-03 9.475e-04 1.747 0.0824 .
Hypertension.compositeyes 4.573e-01 1.170e+00 0.391 0.6963
DiabetesStatusDiabetes -1.386e+00 9.799e-01 -1.415 0.1591
SmokerStatusEx-smoker 4.306e-01 8.720e-01 0.494 0.6221
SmokerStatusNever smoked -4.518e-01 1.123e+00 -0.402 0.6880
Med.Statin.LLDyes 2.460e-01 8.647e-01 0.285 0.7764
Med.all.antiplateletyes 9.678e-01 1.339e+00 0.723 0.4709
GFR_MDRD -1.531e-02 2.056e-02 -0.745 0.4575
BMI 5.775e-02 9.312e-02 0.620 0.5360
MedHx_CVDNo -1.455e-01 7.915e-01 -0.184 0.8544
stenose50-70% 1.242e+00 6.158e+00 0.202 0.8404
stenose70-90% 4.168e+00 4.997e+00 0.834 0.4054
stenose90-99% 3.421e+00 4.968e+00 0.689 0.4921
stenose100% (Occlusion) 4.688e+00 6.276e+00 0.747 0.4561
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.815 on 165 degrees of freedom
Multiple R-squared: 0.0796, Adjusted R-squared: -0.01523
F-statistic: 0.8394 on 17 and 165 DF, p-value: 0.6459
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: IP10_rank
Effect size...............: 0.652391
Standard error............: 0.385601
Odds ratio (effect size)..: 1.92
Lower 95% CI..............: 0.902
Upper 95% CI..............: 4.088
T-value...................: 1.691883
P-value...................: 0.09255598
R^2.......................: 0.079598
Adjusted r^2..............: -0.015231
Sample size of AE DB......: 622
Sample size of model......: 183
Missing data %............: 70.57878
- processing Eotaxin1_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-15.817355 0.001434
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.8560 -2.3946 -1.0691 0.1372 29.5904
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -21.672492 13.798457 -1.571 0.1180
currentDF[, TRAIT] -0.008485 0.353855 -0.024 0.9809
Age -0.033809 0.047934 -0.705 0.4815
Gendermale 0.673504 0.816448 0.825 0.4105
ORdate_epoch 0.001595 0.000917 1.740 0.0836 .
Hypertension.compositeyes 0.264865 1.058910 0.250 0.8028
DiabetesStatusDiabetes -1.196713 0.878665 -1.362 0.1749
SmokerStatusEx-smoker 0.704952 0.787963 0.895 0.3721
SmokerStatusNever smoked -0.156677 1.033423 -0.152 0.8797
Med.Statin.LLDyes 0.234698 0.788180 0.298 0.7662
Med.all.antiplateletyes 0.785151 1.230307 0.638 0.5242
GFR_MDRD -0.009999 0.018803 -0.532 0.5955
BMI 0.048997 0.088029 0.557 0.5785
MedHx_CVDNo -0.046433 0.719449 -0.065 0.9486
stenose50-70% 1.779037 5.563732 0.320 0.7495
stenose70-90% 4.186436 4.826052 0.867 0.3868
stenose90-99% 3.714786 4.820948 0.771 0.4420
stenose100% (Occlusion) 3.655400 6.052589 0.604 0.5466
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.671 on 184 degrees of freedom
Multiple R-squared: 0.05133, Adjusted R-squared: -0.03632
F-statistic: 0.5857 on 17 and 184 DF, p-value: 0.8999
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: Eotaxin1_rank
Effect size...............: -0.008485
Standard error............: 0.353855
Odds ratio (effect size)..: 0.992
Lower 95% CI..............: 0.496
Upper 95% CI..............: 1.984
T-value...................: -0.023978
P-value...................: 0.980896
R^2.......................: 0.051333
Adjusted r^2..............: -0.036315
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing TARC_rankfilter: removed 444 rows (71%), 178 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
2.354
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.0649 -2.4433 -1.2136 0.1619 29.3384
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -21.660845 17.433760 -1.242 0.216
currentDF[, TRAIT] 0.154566 0.417458 0.370 0.712
Age -0.033960 0.055210 -0.615 0.539
Gendermale 0.783335 0.930657 0.842 0.401
ORdate_epoch 0.001505 0.001209 1.245 0.215
Hypertension.compositeyes 0.417704 1.173070 0.356 0.722
DiabetesStatusDiabetes -1.275708 0.982040 -1.299 0.196
SmokerStatusEx-smoker 0.787567 0.890952 0.884 0.378
SmokerStatusNever smoked -0.372759 1.138409 -0.327 0.744
Med.Statin.LLDyes 0.397465 0.926540 0.429 0.669
Med.all.antiplateletyes 1.111156 1.435978 0.774 0.440
GFR_MDRD -0.008284 0.021502 -0.385 0.701
BMI 0.061739 0.099292 0.622 0.535
MedHx_CVDNo -0.032153 0.817727 -0.039 0.969
stenose50-70% 1.726215 6.278650 0.275 0.784
stenose70-90% 4.404426 5.130168 0.859 0.392
stenose90-99% 3.761378 5.113005 0.736 0.463
stenose100% (Occlusion) 4.118905 6.510879 0.633 0.528
Residual standard error: 4.959 on 160 degrees of freedom
Multiple R-squared: 0.05309, Adjusted R-squared: -0.04752
F-statistic: 0.5277 on 17 and 160 DF, p-value: 0.936
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: TARC_rank
Effect size...............: 0.154566
Standard error............: 0.417458
Odds ratio (effect size)..: 1.167
Lower 95% CI..............: 0.515
Upper 95% CI..............: 2.645
T-value...................: 0.370255
P-value...................: 0.7116825
R^2.......................: 0.053093
Adjusted r^2..............: -0.047516
Sample size of AE DB......: 622
Sample size of model......: 178
Missing data %............: 71.38264
- processing PARC_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-22.880077 0.598895 0.001995
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.9525 -2.1971 -1.1485 0.3443 29.6106
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.145e+01 1.460e+01 -2.154 0.0325 *
currentDF[, TRAIT] 6.577e-01 3.714e-01 1.771 0.0782 .
Age -2.609e-02 4.727e-02 -0.552 0.5817
Gendermale 7.179e-01 8.036e-01 0.893 0.3728
ORdate_epoch 2.258e-03 9.687e-04 2.331 0.0209 *
Hypertension.compositeyes 5.558e-01 1.063e+00 0.523 0.6016
DiabetesStatusDiabetes -1.097e+00 8.706e-01 -1.260 0.2091
SmokerStatusEx-smoker 6.481e-01 7.779e-01 0.833 0.4058
SmokerStatusNever smoked -4.426e-01 1.022e+00 -0.433 0.6654
Med.Statin.LLDyes 1.716e-01 7.809e-01 0.220 0.8263
Med.all.antiplateletyes 9.142e-01 1.220e+00 0.749 0.4546
GFR_MDRD -5.706e-03 1.880e-02 -0.304 0.7618
BMI 6.517e-02 8.769e-02 0.743 0.4584
MedHx_CVDNo -9.021e-02 7.125e-01 -0.127 0.8994
stenose50-70% 1.764e+00 5.505e+00 0.321 0.7490
stenose70-90% 4.016e+00 4.776e+00 0.841 0.4014
stenose90-99% 3.628e+00 4.758e+00 0.763 0.4467
stenose100% (Occlusion) 4.473e+00 6.013e+00 0.744 0.4579
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.631 on 184 degrees of freedom
Multiple R-squared: 0.06723, Adjusted R-squared: -0.01895
F-statistic: 0.7801 on 17 and 184 DF, p-value: 0.7145
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: PARC_rank
Effect size...............: 0.657728
Standard error............: 0.371371
Odds ratio (effect size)..: 1.93
Lower 95% CI..............: 0.932
Upper 95% CI..............: 3.997
T-value...................: 1.77108
P-value...................: 0.07820286
R^2.......................: 0.067232
Adjusted r^2..............: -0.018948
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MDC_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-14.625529 0.001345
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.2135 -2.4255 -1.0951 0.1266 29.4807
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.924e+01 1.467e+01 -1.312 0.191
currentDF[, TRAIT] -1.970e-01 3.856e-01 -0.511 0.610
Age -3.818e-02 5.054e-02 -0.755 0.451
Gendermale 8.593e-01 8.701e-01 0.988 0.325
ORdate_epoch 1.364e-03 9.793e-04 1.393 0.165
Hypertension.compositeyes 4.523e-01 1.147e+00 0.394 0.694
DiabetesStatusDiabetes -1.397e+00 9.538e-01 -1.465 0.145
SmokerStatusEx-smoker 6.918e-01 8.433e-01 0.820 0.413
SmokerStatusNever smoked -1.709e-01 1.086e+00 -0.157 0.875
Med.Statin.LLDyes 4.104e-01 8.556e-01 0.480 0.632
Med.all.antiplateletyes 9.305e-01 1.424e+00 0.654 0.514
GFR_MDRD -1.104e-02 1.970e-02 -0.560 0.576
BMI 4.997e-02 9.410e-02 0.531 0.596
MedHx_CVDNo -1.790e-02 7.852e-01 -0.023 0.982
stenose50-70% 2.368e+00 6.163e+00 0.384 0.701
stenose70-90% 4.586e+00 4.979e+00 0.921 0.358
stenose90-99% 4.018e+00 4.962e+00 0.810 0.419
stenose100% (Occlusion) 3.977e+00 6.275e+00 0.634 0.527
Residual standard error: 4.809 on 171 degrees of freedom
Multiple R-squared: 0.056, Adjusted R-squared: -0.03785
F-statistic: 0.5967 on 17 and 171 DF, p-value: 0.8914
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MDC_rank
Effect size...............: -0.197019
Standard error............: 0.38561
Odds ratio (effect size)..: 0.821
Lower 95% CI..............: 0.386
Upper 95% CI..............: 1.749
T-value...................: -0.510929
P-value...................: 0.6100599
R^2.......................: 0.055998
Adjusted r^2..............: -0.037851
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing OPG_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-15.817355 0.001434
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.8452 -2.3654 -1.0829 0.1547 29.6344
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.182e+01 1.364e+01 -1.600 0.111
currentDF[, TRAIT] 8.041e-02 3.411e-01 0.236 0.814
Age -3.173e-02 4.816e-02 -0.659 0.511
Gendermale 6.592e-01 8.114e-01 0.812 0.418
ORdate_epoch 1.595e-03 9.002e-04 1.772 0.078 .
Hypertension.compositeyes 2.861e-01 1.063e+00 0.269 0.788
DiabetesStatusDiabetes -1.190e+00 8.763e-01 -1.358 0.176
SmokerStatusEx-smoker 6.768e-01 7.916e-01 0.855 0.394
SmokerStatusNever smoked -1.942e-01 1.027e+00 -0.189 0.850
Med.Statin.LLDyes 2.242e-01 7.876e-01 0.285 0.776
Med.all.antiplateletyes 7.564e-01 1.233e+00 0.613 0.540
GFR_MDRD -9.987e-03 1.880e-02 -0.531 0.596
BMI 5.090e-02 8.829e-02 0.576 0.565
MedHx_CVDNo -4.633e-02 7.180e-01 -0.065 0.949
stenose50-70% 1.737e+00 5.552e+00 0.313 0.755
stenose70-90% 4.194e+00 4.815e+00 0.871 0.385
stenose90-99% 3.704e+00 4.797e+00 0.772 0.441
stenose100% (Occlusion) 3.761e+00 6.064e+00 0.620 0.536
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.67 on 184 degrees of freedom
Multiple R-squared: 0.05162, Adjusted R-squared: -0.03601
F-statistic: 0.5891 on 17 and 184 DF, p-value: 0.8975
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: OPG_rank
Effect size...............: 0.080414
Standard error............: 0.341108
Odds ratio (effect size)..: 1.084
Lower 95% CI..............: 0.555
Upper 95% CI..............: 2.115
T-value...................: 0.235744
P-value...................: 0.8138933
R^2.......................: 0.051617
Adjusted r^2..............: -0.036006
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing sICAM1_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-15.817355 0.001434
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.9499 -2.3650 -1.0368 0.2082 29.5606
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.828e+01 1.394e+01 -1.311 0.191
currentDF[, TRAIT] -3.760e-01 3.576e-01 -1.051 0.294
Age -4.379e-02 4.831e-02 -0.906 0.366
Gendermale 7.009e-01 8.080e-01 0.867 0.387
ORdate_epoch 1.351e-03 9.262e-04 1.458 0.146
Hypertension.compositeyes 1.477e-01 1.062e+00 0.139 0.890
DiabetesStatusDiabetes -1.264e+00 8.760e-01 -1.443 0.151
SmokerStatusEx-smoker 7.360e-01 7.821e-01 0.941 0.348
SmokerStatusNever smoked 2.786e-02 1.031e+00 0.027 0.978
Med.Statin.LLDyes 2.494e-01 7.846e-01 0.318 0.751
Med.all.antiplateletyes 8.290e-01 1.225e+00 0.677 0.500
GFR_MDRD -1.191e-02 1.883e-02 -0.632 0.528
BMI 5.177e-02 8.774e-02 0.590 0.556
MedHx_CVDNo -1.313e-01 7.206e-01 -0.182 0.856
stenose50-70% 2.032e+00 5.540e+00 0.367 0.714
stenose70-90% 4.659e+00 4.823e+00 0.966 0.335
stenose90-99% 4.161e+00 4.803e+00 0.866 0.388
stenose100% (Occlusion) 4.011e+00 6.038e+00 0.664 0.507
Residual standard error: 4.657 on 184 degrees of freedom
Multiple R-squared: 0.057, Adjusted R-squared: -0.03013
F-statistic: 0.6542 on 17 and 184 DF, p-value: 0.8442
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: sICAM1_rank
Effect size...............: -0.375967
Standard error............: 0.357569
Odds ratio (effect size)..: 0.687
Lower 95% CI..............: 0.341
Upper 95% CI..............: 1.384
T-value...................: -1.051453
P-value...................: 0.2944293
R^2.......................: 0.056996
Adjusted r^2..............: -0.030129
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing VEGFA_rankfilter: removed 445 rows (72%), 177 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-16.593238 0.001508
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.3908 -2.4138 -1.0523 0.2214 29.2614
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -24.500832 15.330316 -1.598 0.1120
currentDF[, TRAIT] -0.241973 0.410471 -0.590 0.5564
Age -0.032729 0.053722 -0.609 0.5432
Gendermale 0.635058 0.910832 0.697 0.4867
ORdate_epoch 0.002003 0.001139 1.757 0.0808 .
Hypertension.compositeyes 0.326664 1.216976 0.268 0.7887
DiabetesStatusDiabetes -1.156998 0.977194 -1.184 0.2382
SmokerStatusEx-smoker 0.953761 0.868794 1.098 0.2739
SmokerStatusNever smoked -0.658051 1.189873 -0.553 0.5810
Med.Statin.LLDyes 0.515954 0.873353 0.591 0.5555
Med.all.antiplateletyes 0.828104 1.304472 0.635 0.5265
GFR_MDRD -0.007824 0.019310 -0.405 0.6859
BMI 0.028931 0.099908 0.290 0.7725
MedHx_CVDNo -0.079548 0.811143 -0.098 0.9220
stenose70-90% 2.097588 3.741795 0.561 0.5759
stenose90-99% 1.641623 3.706914 0.443 0.6585
stenose100% (Occlusion) 1.235058 5.277213 0.234 0.8153
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.878 on 160 degrees of freedom
Multiple R-squared: 0.05871, Adjusted R-squared: -0.03542
F-statistic: 0.6237 on 16 and 160 DF, p-value: 0.8615
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: VEGFA_rank
Effect size...............: -0.241973
Standard error............: 0.410471
Odds ratio (effect size)..: 0.785
Lower 95% CI..............: 0.351
Upper 95% CI..............: 1.755
T-value...................: -0.589501
P-value...................: 0.5563569
R^2.......................: 0.058708
Adjusted r^2..............: -0.035421
Sample size of AE DB......: 622
Sample size of model......: 177
Missing data %............: 71.54341
- processing TGFB_rankfilter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-16.796255 0.001515
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.1150 -2.3911 -1.1547 0.1368 29.4310
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -21.485693 13.521963 -1.589 0.114
currentDF[, TRAIT] -0.137804 0.343232 -0.401 0.689
Age -0.033929 0.047945 -0.708 0.480
Gendermale 0.550802 0.804445 0.685 0.494
ORdate_epoch 0.001597 0.000898 1.778 0.077 .
Hypertension.compositeyes 0.370379 1.045805 0.354 0.724
DiabetesStatusDiabetes -1.190252 0.852732 -1.396 0.164
SmokerStatusEx-smoker 0.758312 0.783326 0.968 0.334
SmokerStatusNever smoked -0.208317 1.039043 -0.200 0.841
Med.Statin.LLDyes 0.326951 0.786036 0.416 0.678
Med.all.antiplateletyes 0.687590 1.263443 0.544 0.587
GFR_MDRD -0.011180 0.018746 -0.596 0.552
BMI 0.040856 0.088697 0.461 0.646
MedHx_CVDNo -0.071165 0.717439 -0.099 0.921
stenose50-70% 2.132694 5.585255 0.382 0.703
stenose70-90% 4.366276 4.823873 0.905 0.367
stenose90-99% 3.894393 4.807602 0.810 0.419
stenose100% (Occlusion) 3.918812 6.098553 0.643 0.521
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.667 on 185 degrees of freedom
Multiple R-squared: 0.05474, Adjusted R-squared: -0.03213
F-statistic: 0.6302 on 17 and 185 DF, p-value: 0.8652
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: TGFB_rank
Effect size...............: -0.137804
Standard error............: 0.343232
Odds ratio (effect size)..: 0.871
Lower 95% CI..............: 0.445
Upper 95% CI..............: 1.707
T-value...................: -0.40149
P-value...................: 0.6885225
R^2.......................: 0.054736
Adjusted r^2..............: -0.032126
Sample size of AE DB......: 622
Sample size of model......: 203
Missing data %............: 67.36334
- processing MMP2_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-17.277671 0.001552
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.0198 -2.2372 -1.0349 0.1006 29.1133
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.880e+01 1.378e+01 -1.365 0.174
currentDF[, TRAIT] -4.068e-01 3.517e-01 -1.157 0.249
Age -4.263e-02 4.690e-02 -0.909 0.365
Gendermale 4.714e-01 7.933e-01 0.594 0.553
ORdate_epoch 1.466e-03 9.095e-04 1.612 0.109
Hypertension.compositeyes 1.569e-01 1.073e+00 0.146 0.884
DiabetesStatusDiabetes -8.140e-01 8.590e-01 -0.948 0.345
SmokerStatusEx-smoker 9.546e-01 7.771e-01 1.228 0.221
SmokerStatusNever smoked 1.283e-01 1.026e+00 0.125 0.901
Med.Statin.LLDyes 1.201e-01 7.717e-01 0.156 0.877
Med.all.antiplateletyes 9.118e-01 1.212e+00 0.753 0.453
GFR_MDRD -1.254e-02 1.798e-02 -0.698 0.486
BMI 3.336e-02 8.940e-02 0.373 0.709
MedHx_CVDNo -2.227e-01 7.117e-01 -0.313 0.755
stenose50-70% 2.042e+00 5.486e+00 0.372 0.710
stenose70-90% 4.324e+00 4.762e+00 0.908 0.365
stenose90-99% 3.694e+00 4.742e+00 0.779 0.437
stenose100% (Occlusion) 3.586e+00 5.983e+00 0.599 0.550
Residual standard error: 4.61 on 184 degrees of freedom
Multiple R-squared: 0.06124, Adjusted R-squared: -0.02549
F-statistic: 0.7061 on 17 and 184 DF, p-value: 0.7943
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MMP2_rank
Effect size...............: -0.406818
Standard error............: 0.351695
Odds ratio (effect size)..: 0.666
Lower 95% CI..............: 0.334
Upper 95% CI..............: 1.326
T-value...................: -1.156733
P-value...................: 0.248881
R^2.......................: 0.06124
Adjusted r^2..............: -0.025494
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MMP8_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-17.277671 0.001552
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.0676 -2.3426 -1.0626 0.1025 29.2867
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.141e+01 1.360e+01 -1.574 0.1172
currentDF[, TRAIT] 1.506e-01 3.408e-01 0.442 0.6591
Age -3.581e-02 4.690e-02 -0.764 0.4461
Gendermale 5.280e-01 8.013e-01 0.659 0.5108
ORdate_epoch 1.640e-03 8.979e-04 1.827 0.0694 .
Hypertension.compositeyes 3.536e-01 1.062e+00 0.333 0.7396
DiabetesStatusDiabetes -7.837e-01 8.682e-01 -0.903 0.3679
SmokerStatusEx-smoker 9.059e-01 7.785e-01 1.164 0.2461
SmokerStatusNever smoked -2.766e-02 1.020e+00 -0.027 0.9784
Med.Statin.LLDyes 1.317e-01 7.741e-01 0.170 0.8651
Med.all.antiplateletyes 8.484e-01 1.214e+00 0.699 0.4857
GFR_MDRD -8.606e-03 1.810e-02 -0.476 0.6350
BMI 3.743e-02 8.977e-02 0.417 0.6772
MedHx_CVDNo -1.682e-01 7.172e-01 -0.234 0.8149
stenose50-70% 1.394e+00 5.543e+00 0.251 0.8017
stenose70-90% 3.694e+00 4.888e+00 0.756 0.4508
stenose90-99% 3.154e+00 4.848e+00 0.651 0.5161
stenose100% (Occlusion) 3.102e+00 6.108e+00 0.508 0.6122
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.625 on 184 degrees of freedom
Multiple R-squared: 0.05542, Adjusted R-squared: -0.03186
F-statistic: 0.635 on 17 and 184 DF, p-value: 0.8611
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MMP8_rank
Effect size...............: 0.150568
Standard error............: 0.340781
Odds ratio (effect size)..: 1.162
Lower 95% CI..............: 0.596
Upper 95% CI..............: 2.267
T-value...................: 0.441831
P-value...................: 0.6591302
R^2.......................: 0.055415
Adjusted r^2..............: -0.031856
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MMP9_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-19.612482 0.494020 0.001739
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.0682 -2.2688 -0.9943 0.1890 28.8994
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.388e+01 1.361e+01 -1.755 0.0810 .
currentDF[, TRAIT] 3.903e-01 3.355e-01 1.163 0.2461
Age -3.156e-02 4.691e-02 -0.673 0.5019
Gendermale 5.279e-01 7.885e-01 0.669 0.5040
ORdate_epoch 1.784e-03 9.011e-04 1.980 0.0492 *
Hypertension.compositeyes 5.669e-01 1.074e+00 0.528 0.5984
DiabetesStatusDiabetes -7.052e-01 8.656e-01 -0.815 0.4163
SmokerStatusEx-smoker 8.623e-01 7.750e-01 1.113 0.2673
SmokerStatusNever smoked -1.172e-01 1.019e+00 -0.115 0.9086
Med.Statin.LLDyes 9.696e-02 7.722e-01 0.126 0.9002
Med.all.antiplateletyes 7.312e-01 1.212e+00 0.603 0.5470
GFR_MDRD -6.109e-03 1.811e-02 -0.337 0.7363
BMI 3.600e-02 8.927e-02 0.403 0.6872
MedHx_CVDNo -1.283e-01 7.141e-01 -0.180 0.8576
stenose50-70% 1.589e+00 5.479e+00 0.290 0.7721
stenose70-90% 3.888e+00 4.766e+00 0.816 0.4157
stenose90-99% 3.343e+00 4.745e+00 0.705 0.4820
stenose100% (Occlusion) 3.520e+00 5.983e+00 0.588 0.5570
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.61 on 184 degrees of freedom
Multiple R-squared: 0.06132, Adjusted R-squared: -0.02541
F-statistic: 0.707 on 17 and 184 DF, p-value: 0.7933
Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CXCL10
Trait/outcome.............: MMP9_rank
Effect size...............: 0.39034
Standard error............: 0.335498
Odds ratio (effect size)..: 1.477
Lower 95% CI..............: 0.765
Upper 95% CI..............: 2.852
T-value...................: 1.163467
P-value...................: 0.2461467
R^2.......................: 0.061319
Adjusted r^2..............: -0.025407
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
Analysis of PCSK9.
- processing IL2_rankfilter: removed 459 rows (74%), 163 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
9.7841230 -0.0007703 1.0501469
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8976 -1.0881 -0.3707 0.4587 11.4538
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.8499630 6.4729132 1.676 0.0958 .
currentDF[, TRAIT] -0.1604819 0.1672839 -0.959 0.3390
Age -0.0369829 0.0223442 -1.655 0.1000
Gendermale 0.0704966 0.4019648 0.175 0.8610
ORdate_epoch -0.0006450 0.0004616 -1.397 0.1645
Hypertension.compositeyes 0.1492126 0.5233539 0.285 0.7760
DiabetesStatusDiabetes -0.2708462 0.4385323 -0.618 0.5378
SmokerStatusEx-smoker 0.5574738 0.3758273 1.483 0.1401
SmokerStatusNever smoked -0.0486884 0.5117104 -0.095 0.9243
Med.Statin.LLDyes -0.5420890 0.3691959 -1.468 0.1442
Med.all.antiplateletyes 0.8292273 0.5850324 1.417 0.1585
GFR_MDRD -0.0106803 0.0096009 -1.112 0.2678
BMI 0.0097137 0.0498126 0.195 0.8457
MedHx_CVDNo -0.2244986 0.3492933 -0.643 0.5214
stenose70-90% 0.4745161 1.4945174 0.318 0.7513
stenose90-99% 0.7412788 1.4849468 0.499 0.6184
stenose100% (Occlusion) 0.0175712 2.1628544 0.008 0.9935
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.993 on 146 degrees of freedom
Multiple R-squared: 0.0971, Adjusted R-squared: -0.00185
F-statistic: 0.9813 on 16 and 146 DF, p-value: 0.4802
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL2_rank
Effect size...............: -0.160482
Standard error............: 0.167284
Odds ratio (effect size)..: 0.852
Lower 95% CI..............: 0.614
Upper 95% CI..............: 1.182
T-value...................: -0.959339
P-value...................: 0.3389741
R^2.......................: 0.097098
Adjusted r^2..............: -0.00185
Sample size of AE DB......: 622
Sample size of model......: 163
Missing data %............: 73.79421
- processing IL4_rankfilter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
9.0634030 -0.0007181 1.1946276
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0907 -1.1247 -0.4529 0.4613 11.2618
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.0164852 7.4199541 1.215 0.227
currentDF[, TRAIT] -0.1152143 0.1915686 -0.601 0.549
Age -0.0390202 0.0251083 -1.554 0.123
Gendermale 0.1287984 0.4485031 0.287 0.774
ORdate_epoch -0.0005674 0.0005323 -1.066 0.288
Hypertension.compositeyes 0.2452435 0.5891099 0.416 0.678
DiabetesStatusDiabetes -0.2463283 0.4900267 -0.503 0.616
SmokerStatusEx-smoker 0.5205554 0.4087910 1.273 0.205
SmokerStatusNever smoked 0.0156696 0.5808852 0.027 0.979
Med.Statin.LLDyes -0.6751317 0.4287609 -1.575 0.118
Med.all.antiplateletyes 1.0021963 0.6435613 1.557 0.122
GFR_MDRD -0.0089924 0.0116919 -0.769 0.443
BMI 0.0311861 0.0585533 0.533 0.595
MedHx_CVDNo -0.2929116 0.3901139 -0.751 0.454
stenose70-90% 0.6228792 1.5870714 0.392 0.695
stenose90-99% 0.9642027 1.5743899 0.612 0.541
stenose100% (Occlusion) 0.4150523 2.3003823 0.180 0.857
Residual standard error: 2.091 on 128 degrees of freedom
Multiple R-squared: 0.103, Adjusted R-squared: -0.009104
F-statistic: 0.9188 on 16 and 128 DF, p-value: 0.5495
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL4_rank
Effect size...............: -0.115214
Standard error............: 0.191569
Odds ratio (effect size)..: 0.891
Lower 95% CI..............: 0.612
Upper 95% CI..............: 1.297
T-value...................: -0.601426
P-value...................: 0.5486202
R^2.......................: 0.103018
Adjusted r^2..............: -0.009104
Sample size of AE DB......: 622
Sample size of model......: 145
Missing data %............: 76.6881
- processing IL5_rankfilter: removed 464 rows (75%), 158 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
11.5056670 -0.0009094 1.0911737
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9380 -1.0818 -0.3361 0.5152 11.3968
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.3386290 6.7398266 2.127 0.0351 *
currentDF[, TRAIT] -0.1978491 0.1718405 -1.151 0.2515
Age -0.0395860 0.0237840 -1.664 0.0983 .
Gendermale 0.1984104 0.4147946 0.478 0.6332
ORdate_epoch -0.0008560 0.0004917 -1.741 0.0839 .
Hypertension.compositeyes 0.3002644 0.5496377 0.546 0.5857
DiabetesStatusDiabetes -0.2743214 0.4467877 -0.614 0.5402
SmokerStatusEx-smoker 0.4160766 0.3778558 1.101 0.2727
SmokerStatusNever smoked -0.0838780 0.5456915 -0.154 0.8781
Med.Statin.LLDyes -0.5945041 0.3887371 -1.529 0.1284
Med.all.antiplateletyes 0.8481359 0.6021601 1.408 0.1612
GFR_MDRD -0.0089028 0.0103260 -0.862 0.3901
BMI 0.0165926 0.0512619 0.324 0.7467
MedHx_CVDNo -0.0981801 0.3666731 -0.268 0.7893
stenose70-90% -0.7390242 1.2691648 -0.582 0.5613
stenose90-99% -0.4162954 1.2536682 -0.332 0.7403
stenose100% (Occlusion) -1.2993230 2.0261230 -0.641 0.5224
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.03 on 141 degrees of freedom
Multiple R-squared: 0.1044, Adjusted R-squared: 0.002754
F-statistic: 1.027 on 16 and 141 DF, p-value: 0.4321
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL5_rank
Effect size...............: -0.197849
Standard error............: 0.17184
Odds ratio (effect size)..: 0.82
Lower 95% CI..............: 0.586
Upper 95% CI..............: 1.149
T-value...................: -1.151353
P-value...................: 0.2515355
R^2.......................: 0.104384
Adjusted r^2..............: 0.002754
Sample size of AE DB......: 622
Sample size of model......: 158
Missing data %............: 74.59807
- processing IL6_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch +
Med.all.antiplatelet, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch Med.all.antiplateletyes
8.1938681 -0.2480591 -0.0006334 0.9617455
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.5585 -1.1573 -0.3255 0.4134 11.5564
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.2721855 6.7266715 1.527 0.129
currentDF[, TRAIT] -0.2627645 0.1612039 -1.630 0.105
Age -0.0356835 0.0235626 -1.514 0.132
Gendermale 0.0893529 0.3877895 0.230 0.818
ORdate_epoch -0.0005268 0.0004482 -1.176 0.242
Hypertension.compositeyes 0.2077785 0.5181299 0.401 0.689
DiabetesStatusDiabetes -0.2356547 0.4335183 -0.544 0.588
SmokerStatusEx-smoker 0.3581933 0.3719235 0.963 0.337
SmokerStatusNever smoked -0.1742868 0.5160158 -0.338 0.736
Med.Statin.LLDyes -0.4819758 0.3665816 -1.315 0.191
Med.all.antiplateletyes 0.7741605 0.5944683 1.302 0.195
GFR_MDRD -0.0066553 0.0100191 -0.664 0.508
BMI 0.0172743 0.0429743 0.402 0.688
MedHx_CVDNo -0.0949186 0.3495195 -0.272 0.786
stenose50-70% -0.3237433 2.3912171 -0.135 0.892
stenose70-90% -0.9805087 2.0775112 -0.472 0.638
stenose90-99% -0.7409162 2.0737036 -0.357 0.721
stenose100% (Occlusion) -1.4186647 2.6177171 -0.542 0.589
Residual standard error: 1.994 on 146 degrees of freedom
Multiple R-squared: 0.0957, Adjusted R-squared: -0.009595
F-statistic: 0.9089 on 17 and 146 DF, p-value: 0.5647
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL6_rank
Effect size...............: -0.262765
Standard error............: 0.161204
Odds ratio (effect size)..: 0.769
Lower 95% CI..............: 0.561
Upper 95% CI..............: 1.055
T-value...................: -1.630013
P-value...................: 0.1052543
R^2.......................: 0.0957
Adjusted r^2..............: -0.009595
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing IL8_rankfilter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
9.344728 -0.000721 1.015887
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0862 -1.1379 -0.3597 0.4404 10.8537
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.9641375 7.3986045 1.617 0.108
currentDF[, TRAIT] 0.1605461 0.1788031 0.898 0.371
Age -0.0314276 0.0241986 -1.299 0.196
Gendermale -0.0614816 0.4320821 -0.142 0.887
ORdate_epoch -0.0006619 0.0004925 -1.344 0.181
Hypertension.compositeyes 0.1522639 0.5359663 0.284 0.777
DiabetesStatusDiabetes -0.2626711 0.4540220 -0.579 0.564
SmokerStatusEx-smoker 0.4129604 0.4056369 1.018 0.310
SmokerStatusNever smoked -0.0241556 0.5837892 -0.041 0.967
Med.Statin.LLDyes -0.3943354 0.3884546 -1.015 0.312
Med.all.antiplateletyes 0.8390684 0.5984857 1.402 0.163
GFR_MDRD -0.0113383 0.0095717 -1.185 0.238
BMI 0.0277434 0.0457343 0.607 0.545
MedHx_CVDNo -0.1125786 0.3786499 -0.297 0.767
stenose50-70% -0.2194598 2.4796278 -0.089 0.930
stenose70-90% -1.2358399 2.1655357 -0.571 0.569
stenose90-99% -0.8121772 2.1576589 -0.376 0.707
stenose100% (Occlusion) -1.1218916 3.0804277 -0.364 0.716
Residual standard error: 2.07 on 136 degrees of freedom
Multiple R-squared: 0.08806, Adjusted R-squared: -0.02593
F-statistic: 0.7725 on 17 and 136 DF, p-value: 0.7217
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL8_rank
Effect size...............: 0.160546
Standard error............: 0.178803
Odds ratio (effect size)..: 1.174
Lower 95% CI..............: 0.827
Upper 95% CI..............: 1.667
T-value...................: 0.897894
P-value...................: 0.3708291
R^2.......................: 0.088059
Adjusted r^2..............: -0.025933
Sample size of AE DB......: 622
Sample size of model......: 154
Missing data %............: 75.24116
- processing IL9_rankfilter: removed 436 rows (70%), 186 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
8.2188013 -0.0006381 1.0688091
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.6582 -1.1038 -0.3021 0.4331 10.9431
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.9267220 5.7409734 1.729 0.0856 .
currentDF[, TRAIT] 0.0781110 0.1445718 0.540 0.5897
Age -0.0367150 0.0210409 -1.745 0.0828 .
Gendermale 0.1326587 0.3487967 0.380 0.7042
ORdate_epoch -0.0004970 0.0003761 -1.321 0.1882
Hypertension.compositeyes 0.2726914 0.4709642 0.579 0.5634
DiabetesStatusDiabetes -0.1415457 0.3898283 -0.363 0.7170
SmokerStatusEx-smoker 0.3978122 0.3407456 1.167 0.2447
SmokerStatusNever smoked -0.1744844 0.4338981 -0.402 0.6881
Med.Statin.LLDyes -0.0938579 0.3445509 -0.272 0.7856
Med.all.antiplateletyes 0.8769745 0.5696162 1.540 0.1255
GFR_MDRD -0.0069358 0.0079266 -0.875 0.3828
BMI 0.0057857 0.0383811 0.151 0.8804
MedHx_CVDNo -0.1721271 0.3149396 -0.547 0.5854
stenose50-70% -0.0272199 2.4511858 -0.011 0.9912
stenose70-90% -1.0184609 1.9863874 -0.513 0.6088
stenose90-99% -0.7429599 1.9780207 -0.376 0.7077
stenose100% (Occlusion) -1.4251375 2.5160945 -0.566 0.5719
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.921 on 168 degrees of freedom
Multiple R-squared: 0.08586, Adjusted R-squared: -0.006646
F-statistic: 0.9282 on 17 and 168 DF, p-value: 0.5421
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL9_rank
Effect size...............: 0.078111
Standard error............: 0.144572
Odds ratio (effect size)..: 1.081
Lower 95% CI..............: 0.814
Upper 95% CI..............: 1.435
T-value...................: 0.540292
P-value...................: 0.5897109
R^2.......................: 0.085857
Adjusted r^2..............: -0.006646
Sample size of AE DB......: 622
Sample size of model......: 186
Missing data %............: 70.09646
- processing IL10_rankfilter: removed 483 rows (78%), 139 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
10.1892459 -0.0008074 1.1598776
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0236 -1.1934 -0.5005 0.5369 11.2467
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.447421 8.117580 1.287 0.201
currentDF[, TRAIT] -0.131923 0.197096 -0.669 0.505
Age -0.038889 0.026484 -1.468 0.145
Gendermale 0.065425 0.466762 0.140 0.889
ORdate_epoch -0.000708 0.000568 -1.246 0.215
Hypertension.compositeyes 0.105998 0.617114 0.172 0.864
DiabetesStatusDiabetes -0.233209 0.509791 -0.457 0.648
SmokerStatusEx-smoker 0.540133 0.430895 1.254 0.212
SmokerStatusNever smoked 0.166542 0.599660 0.278 0.782
Med.Statin.LLDyes -0.616848 0.429729 -1.435 0.154
Med.all.antiplateletyes 0.884548 0.668052 1.324 0.188
GFR_MDRD -0.008202 0.012248 -0.670 0.504
BMI 0.046213 0.060507 0.764 0.446
MedHx_CVDNo -0.315055 0.422730 -0.745 0.458
stenose70-90% 0.602006 1.630495 0.369 0.713
stenose90-99% 0.993206 1.617475 0.614 0.540
stenose100% (Occlusion) 0.366245 2.365797 0.155 0.877
Residual standard error: 2.142 on 122 degrees of freedom
Multiple R-squared: 0.1002, Adjusted R-squared: -0.01778
F-statistic: 0.8493 on 16 and 122 DF, p-value: 0.6278
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL10_rank
Effect size...............: -0.131923
Standard error............: 0.197096
Odds ratio (effect size)..: 0.876
Lower 95% CI..............: 0.596
Upper 95% CI..............: 1.29
T-value...................: -0.669337
P-value...................: 0.5045452
R^2.......................: 0.100225
Adjusted r^2..............: -0.017779
Sample size of AE DB......: 622
Sample size of model......: 139
Missing data %............: 77.65273
- processing IL12_rankfilter: removed 476 rows (77%), 146 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
10.0968678 -0.0007946 1.0821656
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1174 -1.1352 -0.4365 0.5206 11.1735
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.1922214 7.7219594 1.320 0.189
currentDF[, TRAIT] -0.0808842 0.1878581 -0.431 0.668
Age -0.0405068 0.0259160 -1.563 0.121
Gendermale 0.1384201 0.4442982 0.312 0.756
ORdate_epoch -0.0006468 0.0005377 -1.203 0.231
Hypertension.compositeyes 0.0224375 0.6042902 0.037 0.970
DiabetesStatusDiabetes -0.2632880 0.4814394 -0.547 0.585
SmokerStatusEx-smoker 0.5484000 0.4272637 1.284 0.202
SmokerStatusNever smoked -0.0077124 0.5669372 -0.014 0.989
Med.Statin.LLDyes -0.6296595 0.4252754 -1.481 0.141
Med.all.antiplateletyes 0.9202621 0.6463561 1.424 0.157
GFR_MDRD -0.0095510 0.0114392 -0.835 0.405
BMI 0.0336885 0.0578831 0.582 0.562
MedHx_CVDNo -0.2831087 0.3899297 -0.726 0.469
stenose70-90% 0.7150757 1.5827339 0.452 0.652
stenose90-99% 1.0531050 1.5764104 0.668 0.505
stenose100% (Occlusion) 1.0033002 2.7732041 0.362 0.718
Residual standard error: 2.09 on 129 degrees of freedom
Multiple R-squared: 0.1005, Adjusted R-squared: -0.01112
F-statistic: 0.9004 on 16 and 129 DF, p-value: 0.5702
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL12_rank
Effect size...............: -0.080884
Standard error............: 0.187858
Odds ratio (effect size)..: 0.922
Lower 95% CI..............: 0.638
Upper 95% CI..............: 1.333
T-value...................: -0.43056
P-value...................: 0.6675067
R^2.......................: 0.100455
Adjusted r^2..............: -0.011117
Sample size of AE DB......: 622
Sample size of model......: 146
Missing data %............: 76.52733
- processing IL13_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
8.4803387 -0.0006436 0.8802809
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.5061 -1.1357 -0.3728 0.4299 11.0052
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.1066481 5.5578923 1.818 0.0706 .
currentDF[, TRAIT] 0.0224575 0.1431783 0.157 0.8755
Age -0.0302615 0.0197652 -1.531 0.1275
Gendermale 0.0503174 0.3315109 0.152 0.8795
ORdate_epoch -0.0005338 0.0003687 -1.448 0.1494
Hypertension.compositeyes 0.2063253 0.4328991 0.477 0.6342
DiabetesStatusDiabetes -0.1729794 0.3583946 -0.483 0.6299
SmokerStatusEx-smoker 0.3257533 0.3233488 1.007 0.3150
SmokerStatusNever smoked -0.3036498 0.4231855 -0.718 0.4740
Med.Statin.LLDyes -0.1688331 0.3214724 -0.525 0.6001
Med.all.antiplateletyes 0.6365460 0.5005834 1.272 0.2051
GFR_MDRD -0.0080800 0.0076631 -1.054 0.2931
BMI 0.0191669 0.0358691 0.534 0.5937
MedHx_CVDNo 0.0304524 0.2930571 0.104 0.9174
stenose50-70% -0.3756820 2.2717042 -0.165 0.8688
stenose70-90% -1.0068799 1.9666696 -0.512 0.6093
stenose90-99% -0.7803267 1.9644597 -0.397 0.6917
stenose100% (Occlusion) -1.6568699 2.4628016 -0.673 0.5019
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.902 on 184 degrees of freedom
Multiple R-squared: 0.07074, Adjusted R-squared: -0.01511
F-statistic: 0.824 on 17 and 184 DF, p-value: 0.6641
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL13_rank
Effect size...............: 0.022458
Standard error............: 0.143178
Odds ratio (effect size)..: 1.023
Lower 95% CI..............: 0.772
Upper 95% CI..............: 1.354
T-value...................: 0.15685
P-value...................: 0.875535
R^2.......................: 0.070743
Adjusted r^2..............: -0.015113
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing IL21_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
8.4803387 -0.0006436 0.8802809
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.5199 -1.1331 -0.3898 0.4497 11.0197
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.1722243 5.5585042 1.830 0.0689 .
currentDF[, TRAIT] 0.0447558 0.1420252 0.315 0.7530
Age -0.0299205 0.0195853 -1.528 0.1283
Gendermale 0.0433904 0.3320499 0.131 0.8962
ORdate_epoch -0.0005376 0.0003679 -1.461 0.1457
Hypertension.compositeyes 0.2069357 0.4317194 0.479 0.6323
DiabetesStatusDiabetes -0.1697638 0.3578088 -0.474 0.6357
SmokerStatusEx-smoker 0.3221765 0.3213386 1.003 0.3174
SmokerStatusNever smoked -0.3139364 0.4212443 -0.745 0.4571
Med.Statin.LLDyes -0.1746116 0.3219016 -0.542 0.5882
Med.all.antiplateletyes 0.6315310 0.5007826 1.261 0.2089
GFR_MDRD -0.0081156 0.0076559 -1.060 0.2905
BMI 0.0191494 0.0358317 0.534 0.5937
MedHx_CVDNo 0.0322806 0.2928154 0.110 0.9123
stenose50-70% -0.4006546 2.2685660 -0.177 0.8600
stenose70-90% -1.0250053 1.9653242 -0.522 0.6026
stenose90-99% -0.8083071 1.9630100 -0.412 0.6810
stenose100% (Occlusion) -1.6713931 2.4626962 -0.679 0.4982
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.902 on 184 degrees of freedom
Multiple R-squared: 0.07112, Adjusted R-squared: -0.0147
F-statistic: 0.8287 on 17 and 184 DF, p-value: 0.6586
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IL21_rank
Effect size...............: 0.044756
Standard error............: 0.142025
Odds ratio (effect size)..: 1.046
Lower 95% CI..............: 0.792
Upper 95% CI..............: 1.381
T-value...................: 0.315126
P-value...................: 0.7530234
R^2.......................: 0.07112
Adjusted r^2..............: -0.014701
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing INFG_rankfilter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
10.0315552 -0.0007856 1.0670223
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1496 -1.1159 -0.3970 0.4229 11.1084
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.4174786 7.4306247 1.806 0.0732 .
currentDF[, TRAIT] -0.0557169 0.1923920 -0.290 0.7726
Age -0.0389797 0.0244923 -1.592 0.1138
Gendermale 0.0622839 0.4470232 0.139 0.8894
ORdate_epoch -0.0007333 0.0005027 -1.459 0.1469
Hypertension.compositeyes 0.1269517 0.5920853 0.214 0.8305
DiabetesStatusDiabetes -0.3980255 0.4496163 -0.885 0.3776
SmokerStatusEx-smoker 0.5325232 0.4049553 1.315 0.1907
SmokerStatusNever smoked 0.0214474 0.5505104 0.039 0.9690
Med.Statin.LLDyes -0.5832413 0.4002952 -1.457 0.1474
Med.all.antiplateletyes 0.8618680 0.5867285 1.469 0.1442
GFR_MDRD -0.0130252 0.0098962 -1.316 0.1903
BMI 0.0279973 0.0457195 0.612 0.5413
MedHx_CVDNo -0.1992049 0.3859411 -0.516 0.6066
stenose50-70% -0.2654932 2.4776648 -0.107 0.9148
stenose70-90% -1.1291594 2.1615671 -0.522 0.6023
stenose90-99% -0.8469573 2.1495716 -0.394 0.6942
stenose100% (Occlusion) -0.9177005 3.0548773 -0.300 0.7643
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.062 on 136 degrees of freedom
Multiple R-squared: 0.1026, Adjusted R-squared: -0.009566
F-statistic: 0.9147 on 17 and 136 DF, p-value: 0.5581
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: INFG_rank
Effect size...............: -0.055717
Standard error............: 0.192392
Odds ratio (effect size)..: 0.946
Lower 95% CI..............: 0.649
Upper 95% CI..............: 1.379
T-value...................: -0.289601
P-value...................: 0.7725627
R^2.......................: 0.102608
Adjusted r^2..............: -0.009566
Sample size of AE DB......: 622
Sample size of model......: 154
Missing data %............: 75.24116
- processing TNFA_rankfilter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
10.1044919 -0.0007999 1.1605469
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0884 -1.1572 -0.4480 0.5446 11.2659
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.0223039 7.4681209 1.476 0.142
currentDF[, TRAIT] -0.1427858 0.1878069 -0.760 0.448
Age -0.0364955 0.0253706 -1.438 0.153
Gendermale 0.0961476 0.4431679 0.217 0.829
ORdate_epoch -0.0007416 0.0005204 -1.425 0.157
Hypertension.compositeyes 0.1604431 0.5939506 0.270 0.787
DiabetesStatusDiabetes -0.2403900 0.4795920 -0.501 0.617
SmokerStatusEx-smoker 0.5923820 0.4134725 1.433 0.154
SmokerStatusNever smoked 0.0015506 0.5727048 0.003 0.998
Med.Statin.LLDyes -0.5833048 0.4259811 -1.369 0.173
Med.all.antiplateletyes 0.9399976 0.6393322 1.470 0.144
GFR_MDRD -0.0085052 0.0110162 -0.772 0.441
BMI 0.0314197 0.0545572 0.576 0.566
MedHx_CVDNo -0.3603229 0.4009648 -0.899 0.371
stenose70-90% 0.6105518 1.5839803 0.385 0.701
stenose90-99% 0.9857141 1.5678929 0.629 0.531
stenose100% (Occlusion) 1.0378852 2.7626016 0.376 0.708
Residual standard error: 2.094 on 128 degrees of freedom
Multiple R-squared: 0.1047, Adjusted R-squared: -0.007222
F-statistic: 0.9355 on 16 and 128 DF, p-value: 0.531
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: TNFA_rank
Effect size...............: -0.142786
Standard error............: 0.187807
Odds ratio (effect size)..: 0.867
Lower 95% CI..............: 0.6
Upper 95% CI..............: 1.253
T-value...................: -0.76028
P-value...................: 0.4484856
R^2.......................: 0.104691
Adjusted r^2..............: -0.007222
Sample size of AE DB......: 622
Sample size of model......: 145
Missing data %............: 76.6881
- processing MIF_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.all.antiplateletyes
0.5204 0.2758 0.7506
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.3556 -1.1251 -0.3760 0.5035 10.9184
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.9762733 5.9860953 1.165 0.245
currentDF[, TRAIT] 0.2174944 0.1638785 1.327 0.186
Age -0.0287745 0.0193167 -1.490 0.138
Gendermale 0.0338784 0.3287757 0.103 0.918
ORdate_epoch -0.0002809 0.0004096 -0.686 0.494
Hypertension.compositeyes 0.2672490 0.4322416 0.618 0.537
DiabetesStatusDiabetes -0.1471299 0.3559940 -0.413 0.680
SmokerStatusEx-smoker 0.3642270 0.3186260 1.143 0.254
SmokerStatusNever smoked -0.3442418 0.4146906 -0.830 0.408
Med.Statin.LLDyes -0.1854076 0.3193536 -0.581 0.562
Med.all.antiplateletyes 0.5830819 0.4997333 1.167 0.245
GFR_MDRD -0.0063868 0.0077334 -0.826 0.410
BMI 0.0175248 0.0356779 0.491 0.624
MedHx_CVDNo 0.0513931 0.2916814 0.176 0.860
stenose50-70% -0.5573581 2.2565292 -0.247 0.805
stenose70-90% -1.2104885 1.9597502 -0.618 0.538
stenose90-99% -1.0169215 1.9557079 -0.520 0.604
stenose100% (Occlusion) -1.8147752 2.4541232 -0.739 0.461
Residual standard error: 1.894 on 184 degrees of freedom
Multiple R-squared: 0.07943, Adjusted R-squared: -0.005622
F-statistic: 0.9339 on 17 and 184 DF, p-value: 0.5352
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MIF_rank
Effect size...............: 0.217494
Standard error............: 0.163878
Odds ratio (effect size)..: 1.243
Lower 95% CI..............: 0.901
Upper 95% CI..............: 1.714
T-value...................: 1.327169
P-value...................: 0.1860974
R^2.......................: 0.079431
Adjusted r^2..............: -0.005622
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MCP1_rankfilter: removed 422 rows (68%), 200 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
8.599177 -0.000648 0.816759
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.5407 -1.0998 -0.3813 0.4674 11.0932
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.3614211 5.7345598 1.632 0.104
currentDF[, TRAIT] 0.0677775 0.1411637 0.480 0.632
Age -0.0286516 0.0197067 -1.454 0.148
Gendermale 0.0362668 0.3360299 0.108 0.914
ORdate_epoch -0.0004824 0.0003795 -1.271 0.205
Hypertension.compositeyes 0.2252893 0.4369964 0.516 0.607
DiabetesStatusDiabetes -0.1735715 0.3604913 -0.481 0.631
SmokerStatusEx-smoker 0.3300709 0.3217286 1.026 0.306
SmokerStatusNever smoked -0.3245413 0.4213890 -0.770 0.442
Med.Statin.LLDyes -0.1673267 0.3238821 -0.517 0.606
Med.all.antiplateletyes 0.5419435 0.5335727 1.016 0.311
GFR_MDRD -0.0082400 0.0077276 -1.066 0.288
BMI 0.0220328 0.0365758 0.602 0.548
MedHx_CVDNo 0.0245129 0.2958709 0.083 0.934
stenose50-70% -0.3402382 2.2712261 -0.150 0.881
stenose70-90% -1.0053285 1.9703958 -0.510 0.611
stenose90-99% -0.7581317 1.9635063 -0.386 0.700
stenose100% (Occlusion) -1.6711974 2.4809516 -0.674 0.501
Residual standard error: 1.91 on 182 degrees of freedom
Multiple R-squared: 0.0695, Adjusted R-squared: -0.01742
F-statistic: 0.7996 on 17 and 182 DF, p-value: 0.6922
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MCP1_rank
Effect size...............: 0.067777
Standard error............: 0.141164
Odds ratio (effect size)..: 1.07
Lower 95% CI..............: 0.811
Upper 95% CI..............: 1.411
T-value...................: 0.480134
P-value...................: 0.6317087
R^2.......................: 0.069499
Adjusted r^2..............: -0.017416
Sample size of AE DB......: 622
Sample size of model......: 200
Missing data %............: 67.84566
- processing MIP1a_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
7.9495652 -0.0006165 1.0482385
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.5313 -1.0918 -0.2972 0.3988 10.9669
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.6993732 5.6629973 1.713 0.0886 .
currentDF[, TRAIT] 0.0729062 0.1453901 0.501 0.6167
Age -0.0343749 0.0201529 -1.706 0.0899 .
Gendermale 0.1100657 0.3481968 0.316 0.7523
ORdate_epoch -0.0004939 0.0003737 -1.322 0.1881
Hypertension.compositeyes 0.2834033 0.4561234 0.621 0.5352
DiabetesStatusDiabetes -0.2087618 0.3760301 -0.555 0.5795
SmokerStatusEx-smoker 0.3904305 0.3369778 1.159 0.2482
SmokerStatusNever smoked -0.1736360 0.4329765 -0.401 0.6889
Med.Statin.LLDyes -0.0275524 0.3363561 -0.082 0.9348
Med.all.antiplateletyes 0.8170591 0.5646249 1.447 0.1497
GFR_MDRD -0.0073824 0.0078618 -0.939 0.3490
BMI 0.0097185 0.0372298 0.261 0.7944
MedHx_CVDNo -0.1389726 0.3122150 -0.445 0.6568
stenose50-70% 0.0292979 2.4485865 0.012 0.9905
stenose70-90% -1.0575102 1.9827740 -0.533 0.5945
stenose90-99% -0.7909050 1.9795485 -0.400 0.6900
stenose100% (Occlusion) -1.4730931 2.4965846 -0.590 0.5559
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.912 on 171 degrees of freedom
Multiple R-squared: 0.08263, Adjusted R-squared: -0.008569
F-statistic: 0.906 on 17 and 171 DF, p-value: 0.5678
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.072906
Standard error............: 0.14539
Odds ratio (effect size)..: 1.076
Lower 95% CI..............: 0.809
Upper 95% CI..............: 1.43
T-value...................: 0.501452
P-value...................: 0.6166979
R^2.......................: 0.082632
Adjusted r^2..............: -0.008569
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing RANTES_rankfilter: removed 424 rows (68%), 198 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
8.3580186 -0.0006332 0.8650319
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.4977 -1.1406 -0.3838 0.4244 11.0214
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.6913048 5.9698422 1.623 0.106
currentDF[, TRAIT] 0.0140825 0.1566373 0.090 0.928
Age -0.0304608 0.0199360 -1.528 0.128
Gendermale 0.0387236 0.3410783 0.114 0.910
ORdate_epoch -0.0004879 0.0004025 -1.212 0.227
Hypertension.compositeyes 0.1733835 0.4523464 0.383 0.702
DiabetesStatusDiabetes -0.1606232 0.3687753 -0.436 0.664
SmokerStatusEx-smoker 0.3878841 0.3290352 1.179 0.240
SmokerStatusNever smoked -0.2354760 0.4255011 -0.553 0.581
Med.Statin.LLDyes -0.1633726 0.3264400 -0.500 0.617
Med.all.antiplateletyes 0.6457597 0.5325051 1.213 0.227
GFR_MDRD -0.0083758 0.0077818 -1.076 0.283
BMI 0.0146513 0.0367566 0.399 0.691
MedHx_CVDNo -0.0201064 0.3052225 -0.066 0.948
stenose50-70% 0.0124319 2.4354595 0.005 0.996
stenose70-90% -1.0439219 1.9868430 -0.525 0.600
stenose90-99% -0.7814923 1.9768063 -0.395 0.693
stenose100% (Occlusion) -1.6900568 2.5056835 -0.674 0.501
Residual standard error: 1.918 on 180 degrees of freedom
Multiple R-squared: 0.06993, Adjusted R-squared: -0.01791
F-statistic: 0.7962 on 17 and 180 DF, p-value: 0.6962
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: RANTES_rank
Effect size...............: 0.014083
Standard error............: 0.156637
Odds ratio (effect size)..: 1.014
Lower 95% CI..............: 0.746
Upper 95% CI..............: 1.379
T-value...................: 0.089905
P-value...................: 0.9284623
R^2.......................: 0.069934
Adjusted r^2..............: -0.017905
Sample size of AE DB......: 622
Sample size of model......: 198
Missing data %............: 68.1672
- processing MIG_rankfilter: removed 423 rows (68%), 199 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
8.3478925 -0.0006349 0.8846570
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.5195 -1.1005 -0.3751 0.4546 11.0365
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.5299371 5.7497474 1.831 0.0687 .
currentDF[, TRAIT] 0.0865596 0.1546555 0.560 0.5764
Age -0.0286966 0.0197136 -1.456 0.1472
Gendermale 0.0441741 0.3345768 0.132 0.8951
ORdate_epoch -0.0005661 0.0003867 -1.464 0.1450
Hypertension.compositeyes 0.1692077 0.4435762 0.381 0.7033
DiabetesStatusDiabetes -0.2744302 0.3628554 -0.756 0.4504
SmokerStatusEx-smoker 0.2822934 0.3245896 0.870 0.3856
SmokerStatusNever smoked -0.3002840 0.4247399 -0.707 0.4805
Med.Statin.LLDyes -0.0998566 0.3246399 -0.308 0.7587
Med.all.antiplateletyes 0.6301254 0.5102685 1.235 0.2185
GFR_MDRD -0.0093893 0.0076666 -1.225 0.2223
BMI 0.0180473 0.0358039 0.504 0.6148
MedHx_CVDNo -0.0437043 0.2971674 -0.147 0.8832
stenose50-70% 0.0306047 2.4236749 0.013 0.9899
stenose70-90% -0.9446925 1.9602889 -0.482 0.6304
stenose90-99% -0.7699762 1.9558058 -0.394 0.6943
stenose100% (Occlusion) -1.5706709 2.4629317 -0.638 0.5245
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.896 on 181 degrees of freedom
Multiple R-squared: 0.07296, Adjusted R-squared: -0.01411
F-statistic: 0.838 on 17 and 181 DF, p-value: 0.6477
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MIG_rank
Effect size...............: 0.08656
Standard error............: 0.154656
Odds ratio (effect size)..: 1.09
Lower 95% CI..............: 0.805
Upper 95% CI..............: 1.477
T-value...................: 0.559693
P-value...................: 0.5763809
R^2.......................: 0.072961
Adjusted r^2..............: -0.014109
Sample size of AE DB......: 622
Sample size of model......: 199
Missing data %............: 68.00643
- processing IP10_rankfilter: removed 439 rows (71%), 183 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) Age ORdate_epoch Med.all.antiplateletyes
8.7775730 -0.0251532 -0.0005465 1.0418070
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.2665 -1.1059 -0.3324 0.5272 10.8196
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.7258331 5.6923572 1.533 0.1272
currentDF[, TRAIT] -0.0137094 0.1523988 -0.090 0.9284
Age -0.0385512 0.0207998 -1.853 0.0656 .
Gendermale 0.1354236 0.3414521 0.397 0.6922
ORdate_epoch -0.0004052 0.0003745 -1.082 0.2808
Hypertension.compositeyes 0.1808014 0.4622322 0.391 0.6962
DiabetesStatusDiabetes -0.3462517 0.3872862 -0.894 0.3726
SmokerStatusEx-smoker 0.3554329 0.3446304 1.031 0.3039
SmokerStatusNever smoked -0.0966596 0.4438970 -0.218 0.8279
Med.Statin.LLDyes 0.1079288 0.3417310 0.316 0.7525
Med.all.antiplateletyes 0.7758466 0.5292407 1.466 0.1446
GFR_MDRD -0.0055941 0.0081258 -0.688 0.4921
BMI 0.0050427 0.0368020 0.137 0.8912
MedHx_CVDNo -0.1177216 0.3128021 -0.376 0.7071
stenose50-70% 0.2623824 2.4337011 0.108 0.9143
stenose70-90% -0.9234660 1.9747508 -0.468 0.6407
stenose90-99% -0.5665642 1.9635420 -0.289 0.7733
stenose100% (Occlusion) -1.5214714 2.4804733 -0.613 0.5405
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.903 on 165 degrees of freedom
Multiple R-squared: 0.08232, Adjusted R-squared: -0.01223
F-statistic: 0.8707 on 17 and 165 DF, p-value: 0.6093
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: IP10_rank
Effect size...............: -0.013709
Standard error............: 0.152399
Odds ratio (effect size)..: 0.986
Lower 95% CI..............: 0.732
Upper 95% CI..............: 1.33
T-value...................: -0.089958
P-value...................: 0.92843
R^2.......................: 0.082322
Adjusted r^2..............: -0.012227
Sample size of AE DB......: 622
Sample size of model......: 183
Missing data %............: 70.57878
- processing Eotaxin1_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
8.4803387 -0.0006436 0.8802809
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.5582 -1.1404 -0.3850 0.4638 10.9961
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.3587629 5.6192629 1.843 0.0669 .
currentDF[, TRAIT] 0.0488035 0.1441032 0.339 0.7352
Age -0.0299937 0.0195206 -1.537 0.1261
Gendermale 0.0412188 0.3324891 0.124 0.9015
ORdate_epoch -0.0005519 0.0003734 -1.478 0.1412
Hypertension.compositeyes 0.1999904 0.4312289 0.464 0.6434
DiabetesStatusDiabetes -0.1689941 0.3578262 -0.472 0.6373
SmokerStatusEx-smoker 0.3226776 0.3208888 1.006 0.3159
SmokerStatusNever smoked -0.3149659 0.4208498 -0.748 0.4552
Med.Statin.LLDyes -0.1713812 0.3209774 -0.534 0.5940
Med.all.antiplateletyes 0.6292792 0.5010285 1.256 0.2107
GFR_MDRD -0.0080675 0.0076575 -1.054 0.2935
BMI 0.0193759 0.0358487 0.540 0.5895
MedHx_CVDNo 0.0337397 0.2929871 0.115 0.9084
stenose50-70% -0.3928100 2.2657659 -0.173 0.8626
stenose70-90% -1.0285994 1.9653541 -0.523 0.6013
stenose90-99% -0.8139391 1.9632756 -0.415 0.6789
stenose100% (Occlusion) -1.6961631 2.4648471 -0.688 0.4922
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.902 on 184 degrees of freedom
Multiple R-squared: 0.0712, Adjusted R-squared: -0.01462
F-statistic: 0.8297 on 17 and 184 DF, p-value: 0.6575
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.048803
Standard error............: 0.144103
Odds ratio (effect size)..: 1.05
Lower 95% CI..............: 0.792
Upper 95% CI..............: 1.393
T-value...................: 0.33867
P-value...................: 0.7352444
R^2.......................: 0.071197
Adjusted r^2..............: -0.014616
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing TARC_rankfilter: removed 444 rows (71%), 178 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + SmokerStatus +
Med.all.antiplatelet, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] SmokerStatusEx-smoker SmokerStatusNever smoked Med.all.antiplateletyes
0.2120 0.2046 0.5367 -0.1471 0.7674
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.4440 -1.0432 -0.4210 0.4805 8.4707
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.1143480 6.1290244 0.508 0.612
currentDF[, TRAIT] 0.1695653 0.1467620 1.155 0.250
Age -0.0077344 0.0194097 -0.398 0.691
Gendermale 0.4173436 0.3271825 1.276 0.204
ORdate_epoch -0.0001618 0.0004251 -0.381 0.704
Hypertension.compositeyes 0.0349649 0.4124053 0.085 0.933
DiabetesStatusDiabetes -0.1697568 0.3452465 -0.492 0.624
SmokerStatusEx-smoker 0.4468678 0.3132238 1.427 0.156
SmokerStatusNever smoked -0.1801166 0.4002199 -0.450 0.653
Med.Statin.LLDyes -0.2676390 0.3257350 -0.822 0.413
Med.all.antiplateletyes 0.8028104 0.5048335 1.590 0.114
GFR_MDRD -0.0061895 0.0075594 -0.819 0.414
BMI 0.0300940 0.0349072 0.862 0.390
MedHx_CVDNo -0.0384266 0.2874806 -0.134 0.894
stenose50-70% -1.5676876 2.2073263 -0.710 0.479
stenose70-90% -0.8313932 1.8035654 -0.461 0.645
stenose90-99% -0.7803866 1.7975315 -0.434 0.665
stenose100% (Occlusion) -1.1192487 2.2889690 -0.489 0.626
Residual standard error: 1.743 on 160 degrees of freedom
Multiple R-squared: 0.08478, Adjusted R-squared: -0.01246
F-statistic: 0.8719 on 17 and 160 DF, p-value: 0.6078
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: TARC_rank
Effect size...............: 0.169565
Standard error............: 0.146762
Odds ratio (effect size)..: 1.185
Lower 95% CI..............: 0.889
Upper 95% CI..............: 1.58
T-value...................: 1.155377
P-value...................: 0.2496587
R^2.......................: 0.084784
Adjusted r^2..............: -0.012457
Sample size of AE DB......: 622
Sample size of model......: 178
Missing data %............: 71.38264
- processing PARC_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
8.4803387 -0.0006436 0.8802809
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.4010 -1.1118 -0.3694 0.3679 11.1375
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.5825893 5.9897115 1.433 0.154
currentDF[, TRAIT] 0.0981998 0.1523957 0.644 0.520
Age -0.0297714 0.0193996 -1.535 0.127
Gendermale 0.0624007 0.3297507 0.189 0.850
ORdate_epoch -0.0004282 0.0003975 -1.077 0.283
Hypertension.compositeyes 0.2440031 0.4361189 0.559 0.577
DiabetesStatusDiabetes -0.1634999 0.3572421 -0.458 0.648
SmokerStatusEx-smoker 0.3255648 0.3192092 1.020 0.309
SmokerStatusNever smoked -0.3324338 0.4193134 -0.793 0.429
Med.Statin.LLDyes -0.1741226 0.3204623 -0.543 0.588
Med.all.antiplateletyes 0.6588062 0.5006684 1.316 0.190
GFR_MDRD -0.0074872 0.0077133 -0.971 0.333
BMI 0.0213084 0.0359863 0.592 0.554
MedHx_CVDNo 0.0209283 0.2923763 0.072 0.943
stenose50-70% -0.3424317 2.2588627 -0.152 0.880
stenose70-90% -1.0088516 1.9598196 -0.515 0.607
stenose90-99% -0.7605182 1.9524453 -0.390 0.697
stenose100% (Occlusion) -1.5343122 2.4676007 -0.622 0.535
Residual standard error: 1.9 on 184 degrees of freedom
Multiple R-squared: 0.07271, Adjusted R-squared: -0.01296
F-statistic: 0.8487 on 17 and 184 DF, p-value: 0.6352
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: PARC_rank
Effect size...............: 0.0982
Standard error............: 0.152396
Odds ratio (effect size)..: 1.103
Lower 95% CI..............: 0.818
Upper 95% CI..............: 1.487
T-value...................: 0.644374
P-value...................: 0.5201355
R^2.......................: 0.072711
Adjusted r^2..............: -0.012963
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MDC_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.all.antiplateletyes
0.2144 0.2812 1.0225
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7140 -1.0693 -0.2813 0.3677 11.0069
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.6874430 5.8064844 1.324 0.187
currentDF[, TRAIT] 0.2077752 0.1526293 1.361 0.175
Age -0.0330479 0.0200043 -1.652 0.100
Gendermale 0.1092927 0.3443970 0.317 0.751
ORdate_epoch -0.0003392 0.0003876 -0.875 0.383
Hypertension.compositeyes 0.2899076 0.4538702 0.639 0.524
DiabetesStatusDiabetes -0.1643375 0.3775292 -0.435 0.664
SmokerStatusEx-smoker 0.4163006 0.3337993 1.247 0.214
SmokerStatusNever smoked -0.2102146 0.4297679 -0.489 0.625
Med.Statin.LLDyes -0.0937902 0.3386598 -0.277 0.782
Med.all.antiplateletyes 0.8739682 0.5635205 1.551 0.123
GFR_MDRD -0.0071842 0.0077959 -0.922 0.358
BMI 0.0109650 0.0372457 0.294 0.769
MedHx_CVDNo -0.1244947 0.3107730 -0.401 0.689
stenose50-70% -0.2400355 2.4393169 -0.098 0.922
stenose70-90% -1.1481277 1.9707002 -0.583 0.561
stenose90-99% -0.9006567 1.9639923 -0.459 0.647
stenose100% (Occlusion) -1.4665704 2.4836216 -0.590 0.556
Residual standard error: 1.903 on 171 degrees of freedom
Multiple R-squared: 0.09242, Adjusted R-squared: 0.002194
F-statistic: 1.024 on 17 and 171 DF, p-value: 0.4344
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MDC_rank
Effect size...............: 0.207775
Standard error............: 0.152629
Odds ratio (effect size)..: 1.231
Lower 95% CI..............: 0.913
Upper 95% CI..............: 1.66
T-value...................: 1.361306
P-value...................: 0.1752081
R^2.......................: 0.092421
Adjusted r^2..............: 0.002194
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing OPG_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
8.4803387 -0.0006436 0.8802809
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.3823 -1.0481 -0.4359 0.4309 10.7292
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.3746459 5.5441010 1.871 0.0629 .
currentDF[, TRAIT] -0.1297792 0.1386467 -0.936 0.3505
Age -0.0339936 0.0195771 -1.736 0.0842 .
Gendermale 0.0745380 0.3297948 0.226 0.8214
ORdate_epoch -0.0005346 0.0003659 -1.461 0.1457
Hypertension.compositeyes 0.1660542 0.4319120 0.384 0.7011
DiabetesStatusDiabetes -0.1868259 0.3561992 -0.524 0.6006
SmokerStatusEx-smoker 0.3761292 0.3217576 1.169 0.2439
SmokerStatusNever smoked -0.2367943 0.4176290 -0.567 0.5714
Med.Statin.LLDyes -0.1498216 0.3201456 -0.468 0.6404
Med.all.antiplateletyes 0.6829027 0.5012981 1.362 0.1748
GFR_MDRD -0.0081278 0.0076397 -1.064 0.2888
BMI 0.0159704 0.0358854 0.445 0.6568
MedHx_CVDNo 0.0291722 0.2918318 0.100 0.9205
stenose50-70% -0.2882399 2.2567633 -0.128 0.8985
stenose70-90% -1.0087584 1.9571841 -0.515 0.6069
stenose90-99% -0.7502996 1.9499317 -0.385 0.7008
stenose100% (Occlusion) -1.8388629 2.4647512 -0.746 0.4566
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.898 on 184 degrees of freedom
Multiple R-squared: 0.07502, Adjusted R-squared: -0.01044
F-statistic: 0.8779 on 17 and 184 DF, p-value: 0.6008
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: OPG_rank
Effect size...............: -0.129779
Standard error............: 0.138647
Odds ratio (effect size)..: 0.878
Lower 95% CI..............: 0.669
Upper 95% CI..............: 1.153
T-value...................: -0.936042
P-value...................: 0.3504783
R^2.......................: 0.075023
Adjusted r^2..............: -0.010437
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing sICAM1_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
8.4803387 -0.0006436 0.8802809
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.5245 -1.0992 -0.3678 0.4598 11.0038
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.4761057 5.6929815 1.665 0.0977 .
currentDF[, TRAIT] 0.0646077 0.1460203 0.442 0.6587
Age -0.0291581 0.0197288 -1.478 0.1411
Gendermale 0.0502699 0.3299721 0.152 0.8791
ORdate_epoch -0.0004865 0.0003782 -1.286 0.2000
Hypertension.compositeyes 0.2206721 0.4335224 0.509 0.6113
DiabetesStatusDiabetes -0.1662155 0.3577455 -0.465 0.6428
SmokerStatusEx-smoker 0.3280852 0.3194033 1.027 0.3057
SmokerStatusNever smoked -0.3228270 0.4208879 -0.767 0.4441
Med.Statin.LLDyes -0.1675941 0.3203904 -0.523 0.6015
Med.all.antiplateletyes 0.6314403 0.5003615 1.262 0.2086
GFR_MDRD -0.0077964 0.0076901 -1.014 0.3120
BMI 0.0184435 0.0358293 0.515 0.6073
MedHx_CVDNo 0.0423863 0.2942634 0.144 0.8856
stenose50-70% -0.3865843 2.2624979 -0.171 0.8645
stenose70-90% -1.0672027 1.9694918 -0.542 0.5886
stenose90-99% -0.8277846 1.9615781 -0.422 0.6735
stenose100% (Occlusion) -1.7196836 2.4656684 -0.697 0.4864
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.902 on 184 degrees of freedom
Multiple R-squared: 0.07161, Adjusted R-squared: -0.01417
F-statistic: 0.8348 on 17 and 184 DF, p-value: 0.6515
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.064608
Standard error............: 0.14602
Odds ratio (effect size)..: 1.067
Lower 95% CI..............: 0.801
Upper 95% CI..............: 1.42
T-value...................: 0.442457
P-value...................: 0.658678
R^2.......................: 0.071606
Adjusted r^2..............: -0.014169
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing VEGFA_rankfilter: removed 445 rows (72%), 177 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
7.44057 -0.00056 0.82058
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.4898 -1.0462 -0.3979 0.4355 8.6445
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.4076949 5.5460120 1.516 0.131
currentDF[, TRAIT] 0.0030833 0.1484952 0.021 0.983
Age -0.0114980 0.0194348 -0.592 0.555
Gendermale 0.1454983 0.3295097 0.442 0.659
ORdate_epoch -0.0004565 0.0004122 -1.107 0.270
Hypertension.compositeyes 0.0272018 0.4402625 0.062 0.951
DiabetesStatusDiabetes -0.0635258 0.3535171 -0.180 0.858
SmokerStatusEx-smoker 0.4179008 0.3143016 1.330 0.186
SmokerStatusNever smoked 0.0167011 0.4304576 0.039 0.969
Med.Statin.LLDyes -0.3020801 0.3159510 -0.956 0.340
Med.all.antiplateletyes 0.7293476 0.4719158 1.546 0.124
GFR_MDRD -0.0055998 0.0069856 -0.802 0.424
BMI 0.0018191 0.0361433 0.050 0.960
MedHx_CVDNo 0.0469057 0.2934452 0.160 0.873
stenose70-90% -1.1734158 1.3536603 -0.867 0.387
stenose90-99% -1.1737598 1.3410416 -0.875 0.383
stenose100% (Occlusion) -1.8834068 1.9091247 -0.987 0.325
Residual standard error: 1.765 on 160 degrees of freedom
Multiple R-squared: 0.06356, Adjusted R-squared: -0.03008
F-statistic: 0.6788 on 16 and 160 DF, p-value: 0.8119
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.003083
Standard error............: 0.148495
Odds ratio (effect size)..: 1.003
Lower 95% CI..............: 0.75
Upper 95% CI..............: 1.342
T-value...................: 0.020764
P-value...................: 0.9834601
R^2.......................: 0.063562
Adjusted r^2..............: -0.030081
Sample size of AE DB......: 622
Sample size of model......: 177
Missing data %............: 71.54341
- processing TGFB_rankfilter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
8.5969382 -0.0006502 0.8662859
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.5453 -1.1482 -0.3511 0.4641 10.9405
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.0835119 5.4966546 1.834 0.0682 .
currentDF[, TRAIT] -0.0321781 0.1395233 -0.231 0.8179
Age -0.0296836 0.0194897 -1.523 0.1295
Gendermale 0.0149922 0.3270053 0.046 0.9635
ORdate_epoch -0.0005330 0.0003651 -1.460 0.1460
Hypertension.compositeyes 0.2074856 0.4251178 0.488 0.6261
DiabetesStatusDiabetes -0.2805729 0.3466341 -0.809 0.4193
SmokerStatusEx-smoker 0.3552335 0.3184206 1.116 0.2660
SmokerStatusNever smoked -0.2386515 0.4223690 -0.565 0.5727
Med.Statin.LLDyes -0.1765074 0.3195223 -0.552 0.5813
Med.all.antiplateletyes 0.6053299 0.5135875 1.179 0.2401
GFR_MDRD -0.0069050 0.0076202 -0.906 0.3660
BMI 0.0167880 0.0360552 0.466 0.6420
MedHx_CVDNo 0.0130443 0.2916376 0.045 0.9644
stenose50-70% -0.2923283 2.2703965 -0.129 0.8977
stenose70-90% -0.9506879 1.9608958 -0.485 0.6284
stenose90-99% -0.7416988 1.9542817 -0.380 0.7047
stenose100% (Occlusion) -1.6865560 2.4790511 -0.680 0.4972
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.897 on 185 degrees of freedom
Multiple R-squared: 0.06686, Adjusted R-squared: -0.01889
F-statistic: 0.7797 on 17 and 185 DF, p-value: 0.715
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: TGFB_rank
Effect size...............: -0.032178
Standard error............: 0.139523
Odds ratio (effect size)..: 0.968
Lower 95% CI..............: 0.737
Upper 95% CI..............: 1.273
T-value...................: -0.230629
P-value...................: 0.8178578
R^2.......................: 0.066858
Adjusted r^2..............: -0.01889
Sample size of AE DB......: 622
Sample size of model......: 203
Missing data %............: 67.36334
- processing MMP2_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
9.1158316 -0.0006947 0.8370233
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.5502 -1.0774 -0.3665 0.3748 10.7233
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.8165182 5.6240622 2.101 0.0370 *
currentDF[, TRAIT] -0.1455053 0.1435613 -1.014 0.3121
Age -0.0325070 0.0191443 -1.698 0.0912 .
Gendermale 0.0005085 0.3238423 0.002 0.9987
ORdate_epoch -0.0006510 0.0003712 -1.754 0.0812 .
Hypertension.compositeyes 0.1567524 0.4378420 0.358 0.7207
DiabetesStatusDiabetes -0.2355240 0.3506284 -0.672 0.5026
SmokerStatusEx-smoker 0.3928014 0.3171961 1.238 0.2172
SmokerStatusNever smoked -0.1133672 0.4188201 -0.271 0.7869
Med.Statin.LLDyes -0.2805393 0.3150225 -0.891 0.3743
Med.all.antiplateletyes 0.6314303 0.4946244 1.277 0.2034
GFR_MDRD -0.0086571 0.0073387 -1.180 0.2397
BMI 0.0220154 0.0364949 0.603 0.5471
MedHx_CVDNo -0.0927279 0.2905263 -0.319 0.7500
stenose50-70% -0.2265652 2.2393825 -0.101 0.9195
stenose70-90% -0.9968618 1.9440252 -0.513 0.6087
stenose90-99% -0.7937335 1.9356648 -0.410 0.6822
stenose100% (Occlusion) -1.7087291 2.4420600 -0.700 0.4850
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.882 on 184 degrees of freedom
Multiple R-squared: 0.07899, Adjusted R-squared: -0.006099
F-statistic: 0.9283 on 17 and 184 DF, p-value: 0.5417
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MMP2_rank
Effect size...............: -0.145505
Standard error............: 0.143561
Odds ratio (effect size)..: 0.865
Lower 95% CI..............: 0.653
Upper 95% CI..............: 1.146
T-value...................: -1.013542
P-value...................: 0.3121325
R^2.......................: 0.078994
Adjusted r^2..............: -0.006099
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MMP8_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
9.1158316 -0.0006947 0.8370233
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.5848 -1.0344 -0.3781 0.4619 11.0267
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.3313907 5.5256894 2.051 0.0417 *
currentDF[, TRAIT] 0.1793806 0.1384327 1.296 0.1967
Age -0.0289411 0.0190537 -1.519 0.1305
Gendermale -0.0302667 0.3254986 -0.093 0.9260
ORdate_epoch -0.0006022 0.0003648 -1.651 0.1005
Hypertension.compositeyes 0.2261307 0.4314795 0.524 0.6009
DiabetesStatusDiabetes -0.1850047 0.3526851 -0.525 0.6005
SmokerStatusEx-smoker 0.3918467 0.3162410 1.239 0.2169
SmokerStatusNever smoked -0.1668384 0.4143366 -0.403 0.6877
Med.Statin.LLDyes -0.2747123 0.3144564 -0.874 0.3835
Med.all.antiplateletyes 0.6310602 0.4933463 1.279 0.2025
GFR_MDRD -0.0062188 0.0073522 -0.846 0.3987
BMI 0.0207098 0.0364653 0.568 0.5708
MedHx_CVDNo -0.0470137 0.2913370 -0.161 0.8720
stenose50-70% -0.7238109 2.2515305 -0.321 0.7482
stenose70-90% -1.6070731 1.9855952 -0.809 0.4194
stenose90-99% -1.3344907 1.9694917 -0.678 0.4989
stenose100% (Occlusion) -2.3020084 2.4813884 -0.928 0.3548
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.879 on 184 degrees of freedom
Multiple R-squared: 0.08223, Adjusted R-squared: -0.002567
F-statistic: 0.9697 on 17 and 184 DF, p-value: 0.4942
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MMP8_rank
Effect size...............: 0.179381
Standard error............: 0.138433
Odds ratio (effect size)..: 1.196
Lower 95% CI..............: 0.912
Upper 95% CI..............: 1.569
T-value...................: 1.295796
P-value...................: 0.1966688
R^2.......................: 0.082227
Adjusted r^2..............: -0.002567
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MMP9_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch Med.all.antiplateletyes
9.1158316 -0.0006947 0.8370233
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7493 -1.0371 -0.3578 0.4994 11.0687
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.9635022 5.5525423 1.794 0.0744 .
currentDF[, TRAIT] 0.1471965 0.1369075 1.075 0.2837
Age -0.0284371 0.0191412 -1.486 0.1391
Gendermale 0.0195070 0.3217730 0.061 0.9517
ORdate_epoch -0.0005348 0.0003677 -1.454 0.1475
Hypertension.compositeyes 0.3075414 0.4384491 0.701 0.4839
DiabetesStatusDiabetes -0.1941697 0.3532322 -0.550 0.5832
SmokerStatusEx-smoker 0.3593397 0.3162753 1.136 0.2574
SmokerStatusNever smoked -0.2028425 0.4160273 -0.488 0.6264
Med.Statin.LLDyes -0.2894530 0.3151041 -0.919 0.3595
Med.all.antiplateletyes 0.5650494 0.4944474 1.143 0.2546
GFR_MDRD -0.0062837 0.0073910 -0.850 0.3963
BMI 0.0228651 0.0364273 0.628 0.5310
MedHx_CVDNo -0.0575644 0.2913852 -0.198 0.8436
stenose50-70% -0.3909120 2.2359932 -0.175 0.8614
stenose70-90% -1.1580666 1.9447100 -0.595 0.5522
stenose90-99% -0.9236652 1.9361704 -0.477 0.6339
stenose100% (Occlusion) -1.7340090 2.4413862 -0.710 0.4784
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.881 on 184 degrees of freedom
Multiple R-squared: 0.07963, Adjusted R-squared: -0.0054
F-statistic: 0.9365 on 17 and 184 DF, p-value: 0.5322
Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: PCSK9
Trait/outcome.............: MMP9_rank
Effect size...............: 0.147196
Standard error............: 0.136908
Odds ratio (effect size)..: 1.159
Lower 95% CI..............: 0.886
Upper 95% CI..............: 1.515
T-value...................: 1.075153
P-value...................: 0.2837141
R^2.......................: 0.079634
Adjusted r^2..............: -0.0054
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
Analysis of COL4A1.
- processing IL2_rankfilter: removed 459 rows (74%), 163 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
194.2
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-265.51 -120.92 -58.66 43.09 1490.10
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -235.55951 799.85786 -0.295 0.769
currentDF[, TRAIT] -27.02249 20.67127 -1.307 0.193
Age -1.46073 2.76107 -0.529 0.598
Gendermale 36.57245 49.67079 0.736 0.463
ORdate_epoch 0.04164 0.05704 0.730 0.467
Hypertension.compositeyes 30.18003 64.67084 0.467 0.641
DiabetesStatusDiabetes -63.16913 54.18943 -1.166 0.246
SmokerStatusEx-smoker 20.26044 46.44098 0.436 0.663
SmokerStatusNever smoked 22.15109 63.23205 0.350 0.727
Med.Statin.LLDyes -30.66003 45.62153 -0.672 0.503
Med.all.antiplateletyes -16.20899 72.29245 -0.224 0.823
GFR_MDRD -0.18939 1.18639 -0.160 0.873
BMI -2.21988 6.15534 -0.361 0.719
MedHx_CVDNo -15.19596 43.16217 -0.352 0.725
stenose70-90% 84.54864 184.67751 0.458 0.648
stenose90-99% 56.18741 183.49487 0.306 0.760
stenose100% (Occlusion) 165.67079 267.26390 0.620 0.536
Residual standard error: 246.3 on 146 degrees of freedom
Multiple R-squared: 0.04459, Adjusted R-squared: -0.06011
F-statistic: 0.4259 on 16 and 146 DF, p-value: 0.9737
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL2_rank
Effect size...............: -27.02249
Standard error............: 20.67126
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 724467.2
T-value...................: -1.307249
P-value...................: 0.1931832
R^2.......................: 0.044589
Adjusted r^2..............: -0.060114
Sample size of AE DB......: 622
Sample size of model......: 163
Missing data %............: 73.79421
- processing IL4_rankfilter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + DiabetesStatus,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch DiabetesStatusDiabetes
-1096.9488 0.1068 -104.3291
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-262.90 -145.64 -70.33 30.37 1980.90
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -726.39920 1111.92746 -0.653 0.515
currentDF[, TRAIT] -18.42443 28.70778 -0.642 0.522
Age -1.29553 3.76264 -0.344 0.731
Gendermale 82.31981 67.21106 1.225 0.223
ORdate_epoch 0.09498 0.07977 1.191 0.236
Hypertension.compositeyes 71.48231 88.28188 0.810 0.420
DiabetesStatusDiabetes -104.18357 73.43363 -1.419 0.158
SmokerStatusEx-smoker -31.33685 61.25995 -0.512 0.610
SmokerStatusNever smoked -8.37049 87.04936 -0.096 0.924
Med.Statin.LLDyes -31.28422 64.25256 -0.487 0.627
Med.all.antiplateletyes -18.09546 96.44177 -0.188 0.851
GFR_MDRD -1.71086 1.75210 -0.976 0.331
BMI -4.99584 8.77458 -0.569 0.570
MedHx_CVDNo 35.02269 58.46106 0.599 0.550
stenose70-90% 59.65616 237.83277 0.251 0.802
stenose90-99% 19.69623 235.93237 0.083 0.934
stenose100% (Occlusion) 117.55732 344.72696 0.341 0.734
Residual standard error: 313.4 on 128 degrees of freedom
Multiple R-squared: 0.06689, Adjusted R-squared: -0.04975
F-statistic: 0.5735 on 16 and 128 DF, p-value: 0.8988
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL4_rank
Effect size...............: -18.42443
Standard error............: 28.70778
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 2.722238e+16
T-value...................: -0.641792
P-value...................: 0.5221558
R^2.......................: 0.066892
Adjusted r^2..............: -0.049747
Sample size of AE DB......: 622
Sample size of model......: 145
Missing data %............: 76.6881
- processing IL5_rankfilter: removed 464 rows (75%), 158 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
207.79 -43.72
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-285.25 -135.11 -69.41 36.23 2014.53
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -519.27773 1004.69597 -0.517 0.606
currentDF[, TRAIT] -40.85719 25.61601 -1.595 0.113
Age -0.80350 3.54545 -0.227 0.821
Gendermale 74.97786 61.83281 1.213 0.227
ORdate_epoch 0.06409 0.07329 0.874 0.383
Hypertension.compositeyes 46.59942 81.93368 0.569 0.570
DiabetesStatusDiabetes -81.83172 66.60199 -1.229 0.221
SmokerStatusEx-smoker -39.20558 56.32640 -0.696 0.488
SmokerStatusNever smoked -29.29980 81.34543 -0.360 0.719
Med.Statin.LLDyes -8.64441 57.94846 -0.149 0.882
Med.all.antiplateletyes -3.47943 89.76312 -0.039 0.969
GFR_MDRD -0.67672 1.53928 -0.440 0.661
BMI -4.44524 7.64153 -0.582 0.562
MedHx_CVDNo 37.77590 54.65942 0.691 0.491
stenose70-90% 104.07280 189.19251 0.550 0.583
stenose90-99% 78.14027 186.88246 0.418 0.676
stenose100% (Occlusion) 151.12428 302.03115 0.500 0.618
Residual standard error: 302.6 on 141 degrees of freedom
Multiple R-squared: 0.06409, Adjusted R-squared: -0.04211
F-statistic: 0.6035 on 16 and 141 DF, p-value: 0.8771
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL5_rank
Effect size...............: -40.85719
Standard error............: 25.61601
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 11500.87
T-value...................: -1.594987
P-value...................: 0.1129542
R^2.......................: 0.064091
Adjusted r^2..............: -0.042111
Sample size of AE DB......: 622
Sample size of model......: 158
Missing data %............: 74.59807
- processing IL6_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
201.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-244.09 -128.23 -71.25 39.17 2056.11
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -672.79596 1009.07831 -0.667 0.506
currentDF[, TRAIT] -23.42988 24.18245 -0.969 0.334
Age -0.61030 3.53466 -0.173 0.863
Gendermale 59.50579 58.17290 1.023 0.308
ORdate_epoch 0.06128 0.06723 0.912 0.363
Hypertension.compositeyes 36.28956 77.72547 0.467 0.641
DiabetesStatusDiabetes -87.18491 65.03275 -1.341 0.182
SmokerStatusEx-smoker -20.05057 55.79281 -0.359 0.720
SmokerStatusNever smoked -25.49971 77.40832 -0.329 0.742
Med.Statin.LLDyes -0.87523 54.99147 -0.016 0.987
Med.all.antiplateletyes -21.54612 89.17710 -0.242 0.809
GFR_MDRD -1.00792 1.50298 -0.671 0.504
BMI -0.34297 6.44664 -0.053 0.958
MedHx_CVDNo 35.63352 52.43195 0.680 0.498
stenose50-70% 114.44884 358.71015 0.319 0.750
stenose70-90% 199.63081 311.65065 0.641 0.523
stenose90-99% 187.36981 311.07946 0.602 0.548
stenose100% (Occlusion) 307.59523 392.68776 0.783 0.435
Residual standard error: 299.1 on 146 degrees of freedom
Multiple R-squared: 0.04357, Adjusted R-squared: -0.06779
F-statistic: 0.3913 on 17 and 146 DF, p-value: 0.9856
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL6_rank
Effect size...............: -23.42988
Standard error............: 24.18245
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 25647775143
T-value...................: -0.968879
P-value...................: 0.3342076
R^2.......................: 0.043572
Adjusted r^2..............: -0.067793
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing IL8_rankfilter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
193.7
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-265.59 -114.58 -50.70 43.23 2016.10
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -518.81005 951.94117 -0.545 0.5866
currentDF[, TRAIT] 12.54758 23.00569 0.545 0.5864
Age -2.45231 3.11351 -0.788 0.4323
Gendermale 36.36685 55.59383 0.654 0.5141
ORdate_epoch 0.06663 0.06336 1.052 0.2949
Hypertension.compositeyes 7.33503 68.96008 0.106 0.9154
DiabetesStatusDiabetes -84.33342 58.41672 -1.444 0.1511
SmokerStatusEx-smoker -15.71706 52.19125 -0.301 0.7638
SmokerStatusNever smoked -46.22330 75.11322 -0.615 0.5393
Med.Statin.LLDyes -12.43799 49.98050 -0.249 0.8038
Med.all.antiplateletyes 51.04631 77.00414 0.663 0.5085
GFR_MDRD -2.31524 1.23154 -1.880 0.0623 .
BMI -1.19257 5.88440 -0.203 0.8397
MedHx_CVDNo 39.84967 48.71897 0.818 0.4148
stenose50-70% 137.96083 319.04121 0.432 0.6661
stenose70-90% 167.07095 278.62857 0.600 0.5498
stenose90-99% 193.44046 277.61510 0.697 0.4871
stenose100% (Occlusion) 581.55968 396.34312 1.467 0.1446
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 266.3 on 136 degrees of freedom
Multiple R-squared: 0.07732, Adjusted R-squared: -0.03801
F-statistic: 0.6704 on 17 and 136 DF, p-value: 0.8273
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL8_rank
Effect size...............: 12.54758
Standard error............: 23.00569
Odds ratio (effect size)..: 281412.6
Lower 95% CI..............: 0
Upper 95% CI..............: 1.076922e+25
T-value...................: 0.545412
P-value...................: 0.5863637
R^2.......................: 0.077323
Adjusted r^2..............: -0.038012
Sample size of AE DB......: 622
Sample size of model......: 154
Missing data %............: 75.24116
- processing IL9_rankfilter: removed 436 rows (70%), 186 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
208.69 42.63
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-283.67 -135.59 -56.86 16.93 1848.93
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.085e+03 9.061e+02 -1.198 0.2326
currentDF[, TRAIT] 4.897e+01 2.282e+01 2.146 0.0333 *
Age 2.438e+00 3.321e+00 0.734 0.4638
Gendermale 7.297e+01 5.505e+01 1.326 0.1868
ORdate_epoch 6.840e-02 5.936e-02 1.152 0.2509
Hypertension.compositeyes 1.034e+02 7.433e+01 1.392 0.1659
DiabetesStatusDiabetes -8.556e+01 6.152e+01 -1.391 0.1662
SmokerStatusEx-smoker -8.302e+01 5.378e+01 -1.544 0.1245
SmokerStatusNever smoked -1.026e+02 6.848e+01 -1.498 0.1359
Med.Statin.LLDyes 3.291e+01 5.438e+01 0.605 0.5459
Med.all.antiplateletyes -1.454e+01 8.990e+01 -0.162 0.8717
GFR_MDRD -5.388e-01 1.251e+00 -0.431 0.6673
BMI 1.545e-01 6.057e+00 0.026 0.9797
MedHx_CVDNo 7.149e+01 4.970e+01 1.438 0.1522
stenose50-70% 3.519e+01 3.869e+02 0.091 0.9276
stenose70-90% 2.081e+02 3.135e+02 0.664 0.5077
stenose90-99% 1.824e+02 3.122e+02 0.584 0.5598
stenose100% (Occlusion) 3.927e+02 3.971e+02 0.989 0.3241
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 303.2 on 168 degrees of freedom
Multiple R-squared: 0.08982, Adjusted R-squared: -0.00228
F-statistic: 0.9752 on 17 and 168 DF, p-value: 0.4883
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL9_rank
Effect size...............: 48.97253
Standard error............: 22.81682
Odds ratio (effect size)..: 1.855664e+21
Lower 95% CI..............: 70.216
Upper 95% CI..............: 4.904165e+40
T-value...................: 2.146335
P-value...................: 0.03328133
R^2.......................: 0.089822
Adjusted r^2..............: -0.00228
Sample size of AE DB......: 622
Sample size of model......: 186
Missing data %............: 70.09646
- processing IL10_rankfilter: removed 483 rows (78%), 139 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus, data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes
206.9 -87.0
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-285.03 -123.18 -57.37 43.28 1518.05
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -499.23251 994.49557 -0.502 0.617
currentDF[, TRAIT] -14.69025 24.14643 -0.608 0.544
Age -1.14538 3.24460 -0.353 0.725
Gendermale 48.25094 57.18363 0.844 0.400
ORdate_epoch 0.05306 0.06959 0.762 0.447
Hypertension.compositeyes 36.62935 75.60343 0.484 0.629
DiabetesStatusDiabetes -85.33815 62.45513 -1.366 0.174
SmokerStatusEx-smoker 14.25301 52.78951 0.270 0.788
SmokerStatusNever smoked 39.36742 73.46513 0.536 0.593
Med.Statin.LLDyes -35.22363 52.64662 -0.669 0.505
Med.all.antiplateletyes -13.39168 81.84390 -0.164 0.870
GFR_MDRD -0.08557 1.50052 -0.057 0.955
BMI 0.60656 7.41275 0.082 0.935
MedHx_CVDNo -13.96550 51.78926 -0.270 0.788
stenose70-90% 103.94912 199.75411 0.520 0.604
stenose90-99% 56.99770 198.15900 0.288 0.774
stenose100% (Occlusion) 180.87809 289.83695 0.624 0.534
Residual standard error: 262.4 on 122 degrees of freedom
Multiple R-squared: 0.05324, Adjusted R-squared: -0.07092
F-statistic: 0.4288 on 16 and 122 DF, p-value: 0.9722
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL10_rank
Effect size...............: -14.69025
Standard error............: 24.14644
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 1.492683e+14
T-value...................: -0.608382
P-value...................: 0.5440655
R^2.......................: 0.053244
Adjusted r^2..............: -0.070921
Sample size of AE DB......: 622
Sample size of model......: 139
Missing data %............: 77.65273
- processing IL12_rankfilter: removed 476 rows (77%), 146 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
214.39 -44.02
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-292.48 -142.60 -72.27 39.30 1988.42
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -381.27697 1157.08904 -0.330 0.742
currentDF[, TRAIT] -37.66844 28.14940 -1.338 0.183
Age -0.94776 3.88336 -0.244 0.808
Gendermale 81.20956 66.57541 1.220 0.225
ORdate_epoch 0.06289 0.08057 0.781 0.436
Hypertension.compositeyes 41.87433 90.54924 0.462 0.645
DiabetesStatusDiabetes -90.08678 72.14080 -1.249 0.214
SmokerStatusEx-smoker -41.31605 64.02289 -0.645 0.520
SmokerStatusNever smoked -30.93369 84.95212 -0.364 0.716
Med.Statin.LLDyes -30.60346 63.72495 -0.480 0.632
Med.all.antiplateletyes -14.26043 96.85256 -0.147 0.883
GFR_MDRD -0.83930 1.71410 -0.490 0.625
BMI -5.47018 8.67344 -0.631 0.529
MedHx_CVDNo 11.77589 58.42861 0.202 0.841
stenose70-90% 77.07031 237.16313 0.325 0.746
stenose90-99% 39.23630 236.21559 0.166 0.868
stenose100% (Occlusion) 334.04755 415.54790 0.804 0.423
Residual standard error: 313.2 on 129 degrees of freedom
Multiple R-squared: 0.07145, Adjusted R-squared: -0.04372
F-statistic: 0.6204 on 16 and 129 DF, p-value: 0.8628
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL12_rank
Effect size...............: -37.66844
Standard error............: 28.1494
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 39999256
T-value...................: -1.338162
P-value...................: 0.1831977
R^2.......................: 0.071452
Adjusted r^2..............: -0.043717
Sample size of AE DB......: 622
Sample size of model......: 146
Missing data %............: 76.52733
- processing IL13_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + MedHx_CVD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] MedHx_CVDNo
186.20 36.92 63.18
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-295.01 -144.00 -65.03 14.12 1877.75
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.039e+03 8.828e+02 -1.177 0.2408
currentDF[, TRAIT] 4.708e+01 2.274e+01 2.070 0.0398 *
Age 4.121e+00 3.140e+00 1.312 0.1910
Gendermale 6.160e+01 5.266e+01 1.170 0.2436
ORdate_epoch 6.249e-02 5.857e-02 1.067 0.2874
Hypertension.compositeyes 5.572e+01 6.876e+01 0.810 0.4188
DiabetesStatusDiabetes -7.413e+01 5.693e+01 -1.302 0.1945
SmokerStatusEx-smoker -9.553e+01 5.136e+01 -1.860 0.0645 .
SmokerStatusNever smoked -1.153e+02 6.722e+01 -1.715 0.0880 .
Med.Statin.LLDyes 1.682e+01 5.106e+01 0.329 0.7423
Med.all.antiplateletyes -4.355e+01 7.951e+01 -0.548 0.5846
GFR_MDRD -4.827e-01 1.217e+00 -0.397 0.6922
BMI 1.811e+00 5.698e+00 0.318 0.7510
MedHx_CVDNo 6.948e+01 4.655e+01 1.493 0.1372
stenose50-70% 5.818e+01 3.608e+02 0.161 0.8721
stenose70-90% 1.760e+02 3.124e+02 0.563 0.5738
stenose90-99% 1.401e+02 3.120e+02 0.449 0.6539
stenose100% (Occlusion) 3.305e+02 3.912e+02 0.845 0.3993
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 302.2 on 184 degrees of freedom
Multiple R-squared: 0.07791, Adjusted R-squared: -0.007279
F-statistic: 0.9146 on 17 and 184 DF, p-value: 0.5578
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL13_rank
Effect size...............: 47.08246
Standard error............: 22.74296
Odds ratio (effect size)..: 2.803189e+20
Lower 95% CI..............: 12.259
Upper 95% CI..............: 6.409918e+39
T-value...................: 2.070199
P-value...................: 0.03983027
R^2.......................: 0.077913
Adjusted r^2..............: -0.007279
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing IL21_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + MedHx_CVD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] MedHx_CVDNo
186.72 30.23 62.98
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-284.51 -145.05 -69.31 16.11 1905.00
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.058e+03 8.871e+02 -1.193 0.2344
currentDF[, TRAIT] 3.653e+01 2.267e+01 1.612 0.1088
Age 3.581e+00 3.126e+00 1.146 0.2535
Gendermale 6.246e+01 5.299e+01 1.179 0.2400
ORdate_epoch 6.720e-02 5.872e-02 1.144 0.2540
Hypertension.compositeyes 4.883e+01 6.890e+01 0.709 0.4794
DiabetesStatusDiabetes -7.807e+01 5.710e+01 -1.367 0.1733
SmokerStatusEx-smoker -8.820e+01 5.128e+01 -1.720 0.0871 .
SmokerStatusNever smoked -1.067e+02 6.723e+01 -1.587 0.1141
Med.Statin.LLDyes 1.717e+01 5.137e+01 0.334 0.7386
Med.all.antiplateletyes -4.414e+01 7.992e+01 -0.552 0.5814
GFR_MDRD -5.713e-01 1.222e+00 -0.468 0.6406
BMI 1.463e+00 5.718e+00 0.256 0.7984
MedHx_CVDNo 6.735e+01 4.673e+01 1.441 0.1512
stenose50-70% 8.149e+01 3.620e+02 0.225 0.8222
stenose70-90% 1.897e+02 3.137e+02 0.605 0.5460
stenose90-99% 1.571e+02 3.133e+02 0.501 0.6167
stenose100% (Occlusion) 3.179e+02 3.930e+02 0.809 0.4196
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 303.6 on 184 degrees of freedom
Multiple R-squared: 0.06957, Adjusted R-squared: -0.01639
F-statistic: 0.8093 on 17 and 184 DF, p-value: 0.6811
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IL21_rank
Effect size...............: 36.52902
Standard error............: 22.66624
Odds ratio (effect size)..: 7.31729e+15
Lower 95% CI..............: 0
Upper 95% CI..............: 1.439608e+35
T-value...................: 1.611605
P-value...................: 0.108762
R^2.......................: 0.06957
Adjusted r^2..............: -0.016394
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing INFG_rankfilter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + DiabetesStatus,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch DiabetesStatusDiabetes
-943.36164 0.09447 -96.03819
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-247.90 -134.16 -71.69 21.14 2005.78
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.031e+03 1.109e+03 -0.930 0.354
currentDF[, TRAIT] 3.189e+00 2.872e+01 0.111 0.912
Age 3.968e-01 3.657e+00 0.109 0.914
Gendermale 7.930e+01 6.674e+01 1.188 0.237
ORdate_epoch 8.354e-02 7.505e-02 1.113 0.268
Hypertension.compositeyes 2.570e+01 8.840e+01 0.291 0.772
DiabetesStatusDiabetes -9.669e+01 6.713e+01 -1.440 0.152
SmokerStatusEx-smoker -2.931e+01 6.046e+01 -0.485 0.629
SmokerStatusNever smoked -1.262e+01 8.219e+01 -0.154 0.878
Med.Statin.LLDyes -4.047e+00 5.976e+01 -0.068 0.946
Med.all.antiplateletyes 1.969e+01 8.760e+01 0.225 0.822
GFR_MDRD -1.159e+00 1.477e+00 -0.785 0.434
BMI -8.356e-01 6.826e+00 -0.122 0.903
MedHx_CVDNo 4.045e+01 5.762e+01 0.702 0.484
stenose50-70% 1.264e+02 3.699e+02 0.342 0.733
stenose70-90% 2.202e+02 3.227e+02 0.682 0.496
stenose90-99% 1.955e+02 3.209e+02 0.609 0.543
stenose100% (Occlusion) 5.515e+02 4.561e+02 1.209 0.229
Residual standard error: 307.9 on 136 degrees of freedom
Multiple R-squared: 0.05709, Adjusted R-squared: -0.06077
F-statistic: 0.4844 on 17 and 136 DF, p-value: 0.9565
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: INFG_rank
Effect size...............: 3.188903
Standard error............: 28.72333
Odds ratio (effect size)..: 24.262
Lower 95% CI..............: 0
Upper 95% CI..............: 6.834599e+25
T-value...................: 0.111021
P-value...................: 0.9117631
R^2.......................: 0.057094
Adjusted r^2..............: -0.06077
Sample size of AE DB......: 622
Sample size of model......: 154
Missing data %............: 75.24116
- processing TNFA_rankfilter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + DiabetesStatus,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] DiabetesStatusDiabetes
227.08 -40.58 -101.18
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-295.56 -148.59 -68.94 45.75 1986.78
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -199.43719 1113.16229 -0.179 0.858
currentDF[, TRAIT] -40.28898 27.99360 -1.439 0.153
Age -1.04813 3.78162 -0.277 0.782
Gendermale 70.91567 66.05648 1.074 0.285
ORdate_epoch 0.05271 0.07757 0.679 0.498
Hypertension.compositeyes 51.73804 88.53143 0.584 0.560
DiabetesStatusDiabetes -103.90204 71.48569 -1.453 0.149
SmokerStatusEx-smoker -8.23006 61.63023 -0.134 0.894
SmokerStatusNever smoked -19.36882 85.36463 -0.227 0.821
Med.Statin.LLDyes -40.93955 63.49470 -0.645 0.520
Med.all.antiplateletyes -21.38365 95.29580 -0.224 0.823
GFR_MDRD -0.94346 1.64202 -0.575 0.567
BMI -7.07033 8.13204 -0.869 0.386
MedHx_CVDNo 17.60246 59.76589 0.295 0.769
stenose70-90% 71.88210 236.10051 0.304 0.761
stenose90-99% 33.62977 233.70260 0.144 0.886
stenose100% (Occlusion) 334.04664 411.78015 0.811 0.419
Residual standard error: 312.1 on 128 degrees of freedom
Multiple R-squared: 0.07392, Adjusted R-squared: -0.04184
F-statistic: 0.6385 on 16 and 128 DF, p-value: 0.8473
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: TNFA_rank
Effect size...............: -40.28898
Standard error............: 27.9936
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 2144609
T-value...................: -1.439221
P-value...................: 0.1525288
R^2.......................: 0.073918
Adjusted r^2..............: -0.041842
Sample size of AE DB......: 622
Sample size of model......: 145
Missing data %............: 76.6881
- processing MIF_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
211.1
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-266.04 -132.78 -70.50 36.84 1998.92
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.352e+03 9.657e+02 -1.401 0.163
currentDF[, TRAIT] 1.374e+01 2.644e+01 0.520 0.604
Age 2.915e+00 3.116e+00 0.935 0.351
Gendermale 7.091e+01 5.304e+01 1.337 0.183
ORdate_epoch 9.078e-02 6.608e-02 1.374 0.171
Hypertension.compositeyes 4.783e+01 6.973e+01 0.686 0.494
DiabetesStatusDiabetes -8.292e+01 5.743e+01 -1.444 0.150
SmokerStatusEx-smoker -7.682e+01 5.140e+01 -1.495 0.137
SmokerStatusNever smoked -9.089e+01 6.690e+01 -1.359 0.176
Med.Statin.LLDyes 2.382e+01 5.152e+01 0.462 0.644
Med.all.antiplateletyes -4.137e+01 8.062e+01 -0.513 0.608
GFR_MDRD -4.703e-01 1.248e+00 -0.377 0.707
BMI 1.177e+00 5.756e+00 0.205 0.838
MedHx_CVDNo 6.506e+01 4.705e+01 1.383 0.168
stenose50-70% 1.161e+02 3.640e+02 0.319 0.750
stenose70-90% 2.084e+02 3.161e+02 0.659 0.511
stenose90-99% 1.884e+02 3.155e+02 0.597 0.551
stenose100% (Occlusion) 3.193e+02 3.959e+02 0.807 0.421
Residual standard error: 305.5 on 184 degrees of freedom
Multiple R-squared: 0.05782, Adjusted R-squared: -0.02923
F-statistic: 0.6642 on 17 and 184 DF, p-value: 0.8351
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MIF_rank
Effect size...............: 13.74005
Standard error............: 26.43704
Odds ratio (effect size)..: 927318.9
Lower 95% CI..............: 0
Upper 95% CI..............: 2.957279e+28
T-value...................: 0.519727
P-value...................: 0.6038783
R^2.......................: 0.057819
Adjusted r^2..............: -0.02923
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MCP1_rankfilter: removed 422 rows (68%), 200 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
212.4
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-276.84 -141.92 -66.45 24.34 1970.59
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.311e+03 9.187e+02 -1.427 0.155
currentDF[, TRAIT] 1.794e+01 2.261e+01 0.794 0.429
Age 3.434e+00 3.157e+00 1.088 0.278
Gendermale 6.427e+01 5.383e+01 1.194 0.234
ORdate_epoch 8.559e-02 6.079e-02 1.408 0.161
Hypertension.compositeyes 5.334e+01 7.000e+01 0.762 0.447
DiabetesStatusDiabetes -8.357e+01 5.775e+01 -1.447 0.150
SmokerStatusEx-smoker -8.098e+01 5.154e+01 -1.571 0.118
SmokerStatusNever smoked -9.868e+01 6.750e+01 -1.462 0.146
Med.Statin.LLDyes 2.631e+01 5.188e+01 0.507 0.613
Med.all.antiplateletyes -7.023e+01 8.548e+01 -0.822 0.412
GFR_MDRD -5.591e-01 1.238e+00 -0.452 0.652
BMI 1.755e+00 5.859e+00 0.300 0.765
MedHx_CVDNo 6.498e+01 4.740e+01 1.371 0.172
stenose50-70% 1.278e+02 3.638e+02 0.351 0.726
stenose70-90% 2.154e+02 3.156e+02 0.683 0.496
stenose90-99% 1.995e+02 3.145e+02 0.634 0.527
stenose100% (Occlusion) 3.147e+02 3.974e+02 0.792 0.429
Residual standard error: 306 on 182 degrees of freedom
Multiple R-squared: 0.0625, Adjusted R-squared: -0.02506
F-statistic: 0.7138 on 17 and 182 DF, p-value: 0.7864
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MCP1_rank
Effect size...............: 17.94467
Standard error............: 22.6138
Odds ratio (effect size)..: 62125723
Lower 95% CI..............: 0
Upper 95% CI..............: 1.10287e+27
T-value...................: 0.793528
P-value...................: 0.4285039
R^2.......................: 0.062504
Adjusted r^2..............: -0.025064
Sample size of AE DB......: 622
Sample size of model......: 200
Missing data %............: 67.84566
- processing MIP1a_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus, data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes
225.83 -83.25
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-272.22 -140.34 -60.77 17.80 1926.00
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -936.58610 907.69653 -1.032 0.304
currentDF[, TRAIT] 25.72336 23.30393 1.104 0.271
Age 2.10330 3.23022 0.651 0.516
Gendermale 77.56203 55.81091 1.390 0.166
ORdate_epoch 0.05939 0.05990 0.991 0.323
Hypertension.compositeyes 78.53052 73.10998 1.074 0.284
DiabetesStatusDiabetes -93.06918 60.27219 -1.544 0.124
SmokerStatusEx-smoker -71.66308 54.01266 -1.327 0.186
SmokerStatusNever smoked -94.03790 69.39987 -1.355 0.177
Med.Statin.LLDyes 26.89578 53.91301 0.499 0.619
Med.all.antiplateletyes -15.77055 90.50121 -0.174 0.862
GFR_MDRD -0.40702 1.26013 -0.323 0.747
BMI 0.81558 5.96740 0.137 0.891
MedHx_CVDNo 67.25134 50.04354 1.344 0.181
stenose50-70% 45.62049 392.47298 0.116 0.908
stenose70-90% 193.55681 317.80999 0.609 0.543
stenose90-99% 163.34929 317.29298 0.515 0.607
stenose100% (Occlusion) 321.11271 400.16640 0.802 0.423
Residual standard error: 306.4 on 171 degrees of freedom
Multiple R-squared: 0.06715, Adjusted R-squared: -0.02558
F-statistic: 0.7241 on 17 and 171 DF, p-value: 0.7753
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MIP1a_rank
Effect size...............: 25.72336
Standard error............: 23.30393
Odds ratio (effect size)..: 1.48427e+11
Lower 95% CI..............: 0
Upper 95% CI..............: 1.019113e+31
T-value...................: 1.10382
P-value...................: 0.2712219
R^2.......................: 0.067154
Adjusted r^2..............: -0.025585
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing RANTES_rankfilter: removed 424 rows (68%), 198 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch +
MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch MedHx_CVDNo
-960.42384 38.65072 0.09144 66.29043
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-281.07 -141.74 -75.78 29.66 1922.73
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.596e+03 9.539e+02 -1.673 0.0961 .
currentDF[, TRAIT] 3.793e+01 2.503e+01 1.515 0.1314
Age 3.733e+00 3.186e+00 1.172 0.2428
Gendermale 6.345e+01 5.450e+01 1.164 0.2459
ORdate_epoch 1.080e-01 6.431e-02 1.680 0.0947 .
Hypertension.compositeyes 6.112e+01 7.228e+01 0.846 0.3989
DiabetesStatusDiabetes -6.823e+01 5.893e+01 -1.158 0.2484
SmokerStatusEx-smoker -7.752e+01 5.258e+01 -1.475 0.1421
SmokerStatusNever smoked -1.044e+02 6.799e+01 -1.535 0.1265
Med.Statin.LLDyes 2.617e+01 5.216e+01 0.502 0.6164
Med.all.antiplateletyes -5.094e+01 8.509e+01 -0.599 0.5501
GFR_MDRD -3.899e-01 1.243e+00 -0.314 0.7542
BMI 5.503e-01 5.873e+00 0.094 0.9255
MedHx_CVDNo 8.188e+01 4.877e+01 1.679 0.0949 .
stenose50-70% 8.018e+01 3.892e+02 0.206 0.8370
stenose70-90% 1.848e+02 3.175e+02 0.582 0.5612
stenose90-99% 1.677e+02 3.159e+02 0.531 0.5962
stenose100% (Occlusion) 2.685e+02 4.004e+02 0.671 0.5033
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 306.5 on 180 degrees of freedom
Multiple R-squared: 0.07093, Adjusted R-squared: -0.01682
F-statistic: 0.8083 on 17 and 180 DF, p-value: 0.6822
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: RANTES_rank
Effect size...............: 37.92743
Standard error............: 25.02876
Odds ratio (effect size)..: 2.962601e+16
Lower 95% CI..............: 0
Upper 95% CI..............: 5.978322e+37
T-value...................: 1.515354
P-value...................: 0.1314368
R^2.......................: 0.070927
Adjusted r^2..............: -0.016818
Sample size of AE DB......: 622
Sample size of model......: 198
Missing data %............: 68.1672
- processing MIG_rankfilter: removed 423 rows (68%), 199 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
208.95 40.45
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-288.15 -143.42 -62.18 13.04 1880.67
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -688.41763 924.44822 -0.745 0.4574
currentDF[, TRAIT] 48.01073 24.86562 1.931 0.0551 .
Age 3.82958 3.16957 1.208 0.2285
Gendermale 58.53386 53.79349 1.088 0.2780
ORdate_epoch 0.03720 0.06218 0.598 0.5504
Hypertension.compositeyes 52.24472 71.31848 0.733 0.4648
DiabetesStatusDiabetes -77.51555 58.34014 -1.329 0.1856
SmokerStatusEx-smoker -94.65833 52.18773 -1.814 0.0714 .
SmokerStatusNever smoked -114.48722 68.28996 -1.676 0.0954 .
Med.Statin.LLDyes 17.85542 52.19582 0.342 0.7327
Med.all.antiplateletyes -43.24001 82.04131 -0.527 0.5988
GFR_MDRD -0.71943 1.23264 -0.584 0.5602
BMI 1.50435 5.75657 0.261 0.7941
MedHx_CVDNo 68.36653 47.77877 1.431 0.1542
stenose50-70% 36.18597 389.68007 0.093 0.9261
stenose70-90% 190.12868 315.17655 0.603 0.5471
stenose90-99% 156.73558 314.45575 0.498 0.6188
stenose100% (Occlusion) 311.51096 395.99180 0.787 0.4325
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 304.9 on 181 degrees of freedom
Multiple R-squared: 0.07565, Adjusted R-squared: -0.01116
F-statistic: 0.8714 on 17 and 181 DF, p-value: 0.6084
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MIG_rank
Effect size...............: 48.01073
Standard error............: 24.86562
Odds ratio (effect size)..: 7.092462e+20
Lower 95% CI..............: 0.484
Upper 95% CI..............: 1.039533e+42
T-value...................: 1.930808
P-value...................: 0.05506939
R^2.......................: 0.075654
Adjusted r^2..............: -0.011163
Sample size of AE DB......: 622
Sample size of model......: 199
Missing data %............: 68.00643
- processing IP10_rankfilter: removed 439 rows (71%), 183 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_epoch + DiabetesStatus, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_epoch DiabetesStatusDiabetes
-836.52363 45.79817 76.10814 0.08007 -95.08837
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-289.11 -136.20 -57.71 35.76 1780.52
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.196e+03 9.150e+02 -1.307 0.1931
currentDF[, TRAIT] 6.155e+01 2.450e+01 2.513 0.0129 *
Age 3.177e+00 3.343e+00 0.950 0.3433
Gendermale 9.247e+01 5.488e+01 1.685 0.0939 .
ORdate_epoch 7.435e-02 6.019e-02 1.235 0.2185
Hypertension.compositeyes 8.933e+01 7.430e+01 1.202 0.2310
DiabetesStatusDiabetes -8.887e+01 6.225e+01 -1.428 0.1553
SmokerStatusEx-smoker -1.030e+02 5.539e+01 -1.859 0.0649 .
SmokerStatusNever smoked -1.124e+02 7.135e+01 -1.575 0.1172
Med.Statin.LLDyes 2.775e+01 5.493e+01 0.505 0.6141
Med.all.antiplateletyes -2.485e+00 8.507e+01 -0.029 0.9767
GFR_MDRD -6.952e-01 1.306e+00 -0.532 0.5953
BMI 1.022e+00 5.915e+00 0.173 0.8631
MedHx_CVDNo 5.210e+01 5.028e+01 1.036 0.3016
stenose50-70% 1.767e+01 3.912e+02 0.045 0.9640
stenose70-90% 1.894e+02 3.174e+02 0.597 0.5516
stenose90-99% 1.649e+02 3.156e+02 0.523 0.6019
stenose100% (Occlusion) 4.253e+02 3.987e+02 1.067 0.2876
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 305.9 on 165 degrees of freedom
Multiple R-squared: 0.101, Adjusted R-squared: 0.008393
F-statistic: 1.091 on 17 and 165 DF, p-value: 0.3668
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: IP10_rank
Effect size...............: 61.55343
Standard error............: 24.49587
Odds ratio (effect size)..: 5.399024e+26
Lower 95% CI..............: 760347.2
Upper 95% CI..............: 3.833705e+47
T-value...................: 2.512809
P-value...................: 0.01293704
R^2.......................: 0.101016
Adjusted r^2..............: 0.008393
Sample size of AE DB......: 622
Sample size of model......: 183
Missing data %............: 70.57878
- processing Eotaxin1_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
211.1
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-262.23 -137.50 -72.50 22.49 1952.25
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.045e+03 9.017e+02 -1.158 0.248
currentDF[, TRAIT] 1.796e+01 2.312e+01 0.777 0.438
Age 3.115e+00 3.132e+00 0.994 0.321
Gendermale 6.705e+01 5.335e+01 1.257 0.210
ORdate_epoch 6.631e-02 5.992e-02 1.107 0.270
Hypertension.compositeyes 4.341e+01 6.919e+01 0.627 0.531
DiabetesStatusDiabetes -8.152e+01 5.742e+01 -1.420 0.157
SmokerStatusEx-smoker -8.283e+01 5.149e+01 -1.609 0.109
SmokerStatusNever smoked -9.654e+01 6.753e+01 -1.430 0.155
Med.Statin.LLDyes 2.272e+01 5.150e+01 0.441 0.660
Med.all.antiplateletyes -4.150e+01 8.039e+01 -0.516 0.606
GFR_MDRD -5.585e-01 1.229e+00 -0.455 0.650
BMI 1.437e+00 5.752e+00 0.250 0.803
MedHx_CVDNo 6.581e+01 4.701e+01 1.400 0.163
stenose50-70% 1.109e+02 3.636e+02 0.305 0.761
stenose70-90% 2.065e+02 3.154e+02 0.655 0.513
stenose90-99% 1.815e+02 3.150e+02 0.576 0.565
stenose100% (Occlusion) 3.150e+02 3.955e+02 0.797 0.427
Residual standard error: 305.2 on 184 degrees of freedom
Multiple R-squared: 0.05952, Adjusted R-squared: -0.02737
F-statistic: 0.685 on 17 and 184 DF, p-value: 0.8153
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 17.96428
Standard error............: 23.1227
Odds ratio (effect size)..: 63355847
Lower 95% CI..............: 0
Upper 95% CI..............: 3.049465e+27
T-value...................: 0.776911
P-value...................: 0.4382089
R^2.......................: 0.059521
Adjusted r^2..............: -0.027371
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing TARC_rankfilter: removed 444 rows (71%), 178 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + MedHx_CVD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] MedHx_CVDNo
195.56 35.20 71.33
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-312.94 -149.97 -70.46 32.93 1838.11
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.739e+03 1.119e+03 -1.553 0.1223
currentDF[, TRAIT] 5.468e+01 2.680e+01 2.040 0.0430 *
Age 5.479e+00 3.545e+00 1.545 0.1242
Gendermale 9.475e+01 5.975e+01 1.586 0.1148
ORdate_epoch 1.034e-01 7.765e-02 1.332 0.1848
Hypertension.compositeyes 6.470e+01 7.532e+01 0.859 0.3917
DiabetesStatusDiabetes -8.152e+01 6.305e+01 -1.293 0.1979
SmokerStatusEx-smoker -8.915e+01 5.721e+01 -1.558 0.1211
SmokerStatusNever smoked -1.261e+02 7.309e+01 -1.726 0.0863 .
Med.Statin.LLDyes 9.712e+00 5.949e+01 0.163 0.8705
Med.all.antiplateletyes -3.634e+01 9.220e+01 -0.394 0.6940
GFR_MDRD -6.769e-02 1.381e+00 -0.049 0.9610
BMI 1.143e+00 6.375e+00 0.179 0.8579
MedHx_CVDNo 7.814e+01 5.250e+01 1.488 0.1387
stenose50-70% 1.671e+02 4.031e+02 0.415 0.6790
stenose70-90% 2.413e+02 3.294e+02 0.733 0.4648
stenose90-99% 1.848e+02 3.283e+02 0.563 0.5743
stenose100% (Occlusion) 3.656e+02 4.180e+02 0.874 0.3832
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 318.4 on 160 degrees of freedom
Multiple R-squared: 0.08749, Adjusted R-squared: -0.009463
F-statistic: 0.9024 on 17 and 160 DF, p-value: 0.5722
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: TARC_rank
Effect size...............: 54.67634
Standard error............: 26.80373
Odds ratio (effect size)..: 5.567137e+23
Lower 95% CI..............: 8.508
Upper 95% CI..............: 3.642712e+46
T-value...................: 2.039878
P-value...................: 0.04300705
R^2.......................: 0.087491
Adjusted r^2..............: -0.009463
Sample size of AE DB......: 622
Sample size of model......: 178
Missing data %............: 71.38264
- processing PARC_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-1071.1425 42.2875 0.1021
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-281.93 -144.01 -67.22 37.34 1908.50
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.882e+03 9.532e+02 -1.974 0.0499 *
currentDF[, TRAIT] 4.843e+01 2.425e+01 1.997 0.0473 *
Age 3.338e+00 3.087e+00 1.081 0.2811
Gendermale 7.572e+01 5.248e+01 1.443 0.1507
ORdate_epoch 1.243e-01 6.326e-02 1.964 0.0510 .
Hypertension.compositeyes 6.505e+01 6.940e+01 0.937 0.3499
DiabetesStatusDiabetes -7.767e+01 5.685e+01 -1.366 0.1735
SmokerStatusEx-smoker -8.279e+01 5.080e+01 -1.630 0.1049
SmokerStatusNever smoked -1.082e+02 6.673e+01 -1.622 0.1065
Med.Statin.LLDyes 2.056e+01 5.100e+01 0.403 0.6874
Med.all.antiplateletyes -2.819e+01 7.968e+01 -0.354 0.7239
GFR_MDRD -2.649e-01 1.227e+00 -0.216 0.8294
BMI 2.449e+00 5.727e+00 0.428 0.6694
MedHx_CVDNo 6.025e+01 4.653e+01 1.295 0.1970
stenose50-70% 1.293e+02 3.595e+02 0.360 0.7195
stenose70-90% 2.107e+02 3.119e+02 0.676 0.5001
stenose90-99% 1.997e+02 3.107e+02 0.643 0.5211
stenose100% (Occlusion) 3.900e+02 3.927e+02 0.993 0.3219
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 302.4 on 184 degrees of freedom
Multiple R-squared: 0.07645, Adjusted R-squared: -0.008876
F-statistic: 0.896 on 17 and 184 DF, p-value: 0.5795
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: PARC_rank
Effect size...............: 48.42948
Standard error............: 24.25197
Odds ratio (effect size)..: 1.078096e+21
Lower 95% CI..............: 2.449
Upper 95% CI..............: 4.746238e+41
T-value...................: 1.99693
P-value...................: 0.04730674
R^2.......................: 0.076452
Adjusted r^2..............: -0.008876
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MDC_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
213.1
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-272.66 -136.16 -71.55 22.83 1915.88
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.381e+03 9.396e+02 -1.470 0.143
currentDF[, TRAIT] 3.086e+01 2.470e+01 1.250 0.213
Age 2.608e+00 3.237e+00 0.806 0.422
Gendermale 8.557e+01 5.573e+01 1.535 0.127
ORdate_epoch 8.779e-02 6.272e-02 1.400 0.163
Hypertension.compositeyes 8.475e+01 7.344e+01 1.154 0.250
DiabetesStatusDiabetes -7.412e+01 6.109e+01 -1.213 0.227
SmokerStatusEx-smoker -7.518e+01 5.401e+01 -1.392 0.166
SmokerStatusNever smoked -1.009e+02 6.954e+01 -1.451 0.149
Med.Statin.LLDyes 2.374e+01 5.480e+01 0.433 0.665
Med.all.antiplateletyes -1.800e+00 9.118e+01 -0.020 0.984
GFR_MDRD -1.460e-01 1.261e+00 -0.116 0.908
BMI 9.592e-01 6.027e+00 0.159 0.874
MedHx_CVDNo 6.480e+01 5.029e+01 1.289 0.199
stenose50-70% 3.273e+01 3.947e+02 0.083 0.934
stenose70-90% 2.019e+02 3.189e+02 0.633 0.528
stenose90-99% 1.779e+02 3.178e+02 0.560 0.576
stenose100% (Occlusion) 3.455e+02 4.019e+02 0.860 0.391
Residual standard error: 308 on 171 degrees of freedom
Multiple R-squared: 0.06725, Adjusted R-squared: -0.02548
F-statistic: 0.7252 on 17 and 171 DF, p-value: 0.7742
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MDC_rank
Effect size...............: 30.86294
Standard error............: 24.69703
Odds ratio (effect size)..: 2.532825e+13
Lower 95% CI..............: 0
Upper 95% CI..............: 2.667721e+34
T-value...................: 1.249662
P-value...................: 0.2131307
R^2.......................: 0.067247
Adjusted r^2..............: -0.025482
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing OPG_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
211.1
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-275.05 -143.48 -69.30 26.87 1890.52
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1259.6408 884.8948 -1.423 0.156
currentDF[, TRAIT] 40.4710 22.1294 1.829 0.069 .
Age 3.7454 3.1247 1.199 0.232
Gendermale 66.2998 52.6386 1.260 0.209
ORdate_epoch 0.0773 0.0584 1.324 0.187
Hypertension.compositeyes 54.3780 68.9375 0.789 0.431
DiabetesStatusDiabetes -82.1583 56.8530 -1.445 0.150
SmokerStatusEx-smoker -91.9595 51.3558 -1.791 0.075 .
SmokerStatusNever smoked -104.1995 66.6578 -1.563 0.120
Med.Statin.LLDyes 20.4220 51.0985 0.400 0.690
Med.all.antiplateletyes -51.4252 80.0123 -0.643 0.521
GFR_MDRD -0.5798 1.2194 -0.476 0.635
BMI 2.1802 5.7277 0.381 0.704
MedHx_CVDNo 63.0706 46.5793 1.354 0.177
stenose50-70% 113.1068 360.2023 0.314 0.754
stenose70-90% 230.2347 312.3865 0.737 0.462
stenose90-99% 205.6173 311.2289 0.661 0.510
stenose100% (Occlusion) 385.8549 393.3993 0.981 0.328
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 303 on 184 degrees of freedom
Multiple R-squared: 0.07328, Adjusted R-squared: -0.01234
F-statistic: 0.8559 on 17 and 184 DF, p-value: 0.6267
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: OPG_rank
Effect size...............: 40.47104
Standard error............: 22.12942
Odds ratio (effect size)..: 3.770081e+17
Lower 95% CI..............: 0.055
Upper 95% CI..............: 2.58998e+36
T-value...................: 1.828834
P-value...................: 0.06904338
R^2.......................: 0.073281
Adjusted r^2..............: -0.012339
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing sICAM1_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
211.1
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-267.70 -131.57 -76.38 32.01 1975.79
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.135e+03 9.152e+02 -1.240 0.216
currentDF[, TRAIT] -2.627e+00 2.347e+01 -0.112 0.911
Age 2.710e+00 3.171e+00 0.854 0.394
Gendermale 7.248e+01 5.304e+01 1.366 0.174
ORdate_epoch 7.351e-02 6.080e-02 1.209 0.228
Hypertension.compositeyes 4.280e+01 6.969e+01 0.614 0.540
DiabetesStatusDiabetes -8.536e+01 5.751e+01 -1.484 0.139
SmokerStatusEx-smoker -7.852e+01 5.134e+01 -1.529 0.128
SmokerStatusNever smoked -8.617e+01 6.766e+01 -1.274 0.204
Med.Statin.LLDyes 2.523e+01 5.150e+01 0.490 0.625
Med.all.antiplateletyes -3.750e+01 8.043e+01 -0.466 0.642
GFR_MDRD -5.937e-01 1.236e+00 -0.480 0.632
BMI 1.283e+00 5.760e+00 0.223 0.824
MedHx_CVDNo 6.295e+01 4.730e+01 1.331 0.185
stenose50-70% 1.316e+02 3.637e+02 0.362 0.718
stenose70-90% 2.261e+02 3.166e+02 0.714 0.476
stenose90-99% 2.085e+02 3.153e+02 0.661 0.509
stenose100% (Occlusion) 3.318e+02 3.964e+02 0.837 0.404
Residual standard error: 305.7 on 184 degrees of freedom
Multiple R-squared: 0.0565, Adjusted R-squared: -0.03067
F-statistic: 0.6482 on 17 and 184 DF, p-value: 0.8496
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: sICAM1_rank
Effect size...............: -2.626836
Standard error............: 23.47307
Odds ratio (effect size)..: 0.072
Lower 95% CI..............: 0
Upper 95% CI..............: 6.916092e+18
T-value...................: -0.111909
P-value...................: 0.9110179
R^2.......................: 0.0565
Adjusted r^2..............: -0.030671
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing VEGFA_rankfilter: removed 445 rows (72%), 177 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
210.7
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-276.30 -135.41 -71.76 23.14 2007.12
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -853.61108 932.86902 -0.915 0.362
currentDF[, TRAIT] -2.04485 24.97769 -0.082 0.935
Age 2.59380 3.26903 0.793 0.429
Gendermale 80.22425 55.42530 1.447 0.150
ORdate_epoch 0.06808 0.06934 0.982 0.328
Hypertension.compositeyes 32.40750 74.05453 0.438 0.662
DiabetesStatusDiabetes -76.07754 59.46348 -1.279 0.203
SmokerStatusEx-smoker -49.41605 52.86722 -0.935 0.351
SmokerStatusNever smoked -62.12856 72.40528 -0.858 0.392
Med.Statin.LLDyes 16.27838 53.14466 0.306 0.760
Med.all.antiplateletyes -33.22609 79.37878 -0.419 0.676
GFR_MDRD -0.72574 1.17502 -0.618 0.538
BMI -0.65980 6.07950 -0.109 0.914
MedHx_CVDNo 48.78172 49.35907 0.988 0.324
stenose70-90% 91.13613 227.69293 0.400 0.689
stenose90-99% 46.24330 225.57040 0.205 0.838
stenose100% (Occlusion) 168.64747 321.12503 0.525 0.600
Residual standard error: 296.9 on 160 degrees of freedom
Multiple R-squared: 0.04777, Adjusted R-squared: -0.04746
F-statistic: 0.5016 on 16 and 160 DF, p-value: 0.9437
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: VEGFA_rank
Effect size...............: -2.044846
Standard error............: 24.97769
Odds ratio (effect size)..: 0.129
Lower 95% CI..............: 0
Upper 95% CI..............: 2.362516e+20
T-value...................: -0.081867
P-value...................: 0.9348549
R^2.......................: 0.047767
Adjusted r^2..............: -0.047456
Sample size of AE DB......: 622
Sample size of model......: 177
Missing data %............: 71.54341
- processing TGFB_rankfilter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus, data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes
229.98 -76.53
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-266.43 -137.16 -71.91 34.77 1977.05
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.142e+03 8.825e+02 -1.294 0.197
currentDF[, TRAIT] 3.206e+00 2.240e+01 0.143 0.886
Age 2.810e+00 3.129e+00 0.898 0.370
Gendermale 6.952e+01 5.250e+01 1.324 0.187
ORdate_epoch 7.564e-02 5.861e-02 1.291 0.198
Hypertension.compositeyes 5.100e+01 6.825e+01 0.747 0.456
DiabetesStatusDiabetes -8.887e+01 5.565e+01 -1.597 0.112
SmokerStatusEx-smoker -7.190e+01 5.112e+01 -1.406 0.161
SmokerStatusNever smoked -8.401e+01 6.781e+01 -1.239 0.217
Med.Statin.LLDyes 2.976e+01 5.130e+01 0.580 0.563
Med.all.antiplateletyes -5.007e+01 8.245e+01 -0.607 0.544
GFR_MDRD -5.982e-01 1.223e+00 -0.489 0.625
BMI 7.023e-01 5.789e+00 0.121 0.904
MedHx_CVDNo 6.149e+01 4.682e+01 1.313 0.191
stenose50-70% 1.298e+02 3.645e+02 0.356 0.722
stenose70-90% 2.205e+02 3.148e+02 0.701 0.484
stenose90-99% 2.021e+02 3.138e+02 0.644 0.520
stenose100% (Occlusion) 3.088e+02 3.980e+02 0.776 0.439
Residual standard error: 304.6 on 185 degrees of freedom
Multiple R-squared: 0.05856, Adjusted R-squared: -0.02795
F-statistic: 0.6769 on 17 and 185 DF, p-value: 0.8232
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: TGFB_rank
Effect size...............: 3.20586
Standard error............: 22.40001
Odds ratio (effect size)..: 24.677
Lower 95% CI..............: 0
Upper 95% CI..............: 2.881106e+20
T-value...................: 0.143119
P-value...................: 0.8863522
R^2.......................: 0.058556
Adjusted r^2..............: -0.027955
Sample size of AE DB......: 622
Sample size of model......: 203
Missing data %............: 67.36334
- processing MMP2_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
200.8
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-231.09 -124.15 -69.20 31.07 2032.68
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -465.58466 837.90888 -0.556 0.579
currentDF[, TRAIT] -4.91769 21.38868 -0.230 0.818
Age 0.92295 2.85224 0.324 0.747
Gendermale 50.27334 48.24810 1.042 0.299
ORdate_epoch 0.04232 0.05531 0.765 0.445
Hypertension.compositeyes 28.85110 65.23252 0.442 0.659
DiabetesStatusDiabetes -75.75530 52.23887 -1.450 0.149
SmokerStatusEx-smoker -35.14394 47.25791 -0.744 0.458
SmokerStatusNever smoked -42.25281 62.39851 -0.677 0.499
Med.Statin.LLDyes 7.63101 46.93408 0.163 0.871
Med.all.antiplateletyes -45.77690 73.69231 -0.621 0.535
GFR_MDRD -1.11466 1.09337 -1.019 0.309
BMI -0.53654 5.43725 -0.099 0.922
MedHx_CVDNo 43.21482 43.28448 0.998 0.319
stenose50-70% 105.44204 333.63757 0.316 0.752
stenose70-90% 177.08739 289.63336 0.611 0.542
stenose90-99% 153.79478 288.38776 0.533 0.594
stenose100% (Occlusion) 258.64560 363.83377 0.711 0.478
Residual standard error: 280.4 on 184 degrees of freedom
Multiple R-squared: 0.04077, Adjusted R-squared: -0.04785
F-statistic: 0.4601 on 17 and 184 DF, p-value: 0.9672
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MMP2_rank
Effect size...............: -4.917694
Standard error............: 21.38868
Odds ratio (effect size)..: 0.007
Lower 95% CI..............: 0
Upper 95% CI..............: 1.176749e+16
T-value...................: -0.22992
P-value...................: 0.818409
R^2.......................: 0.040772
Adjusted r^2..............: -0.047852
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MMP8_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
200.8
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-246.06 -126.45 -69.60 23.11 2034.61
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -468.09964 824.30055 -0.568 0.571
currentDF[, TRAIT] 9.94572 20.65085 0.482 0.631
Age 1.07830 2.84236 0.379 0.705
Gendermale 47.65535 48.55660 0.981 0.328
ORdate_epoch 0.04356 0.05441 0.800 0.424
Hypertension.compositeyes 31.16563 64.36641 0.484 0.629
DiabetesStatusDiabetes -72.82029 52.61217 -1.384 0.168
SmokerStatusEx-smoker -34.66688 47.17559 -0.735 0.463
SmokerStatusNever smoked -43.98859 61.80910 -0.712 0.478
Med.Statin.LLDyes 7.87987 46.90937 0.168 0.867
Med.all.antiplateletyes -45.09910 73.59546 -0.613 0.541
GFR_MDRD -1.00036 1.09678 -0.912 0.363
BMI -0.66604 5.43975 -0.122 0.903
MedHx_CVDNo 45.57016 43.46050 1.049 0.296
stenose50-70% 80.42648 335.87444 0.239 0.811
stenose70-90% 144.55833 296.20326 0.488 0.626
stenose90-99% 124.75982 293.80101 0.425 0.672
stenose100% (Occlusion) 225.59945 370.16373 0.609 0.543
Residual standard error: 280.3 on 184 degrees of freedom
Multiple R-squared: 0.0417, Adjusted R-squared: -0.04683
F-statistic: 0.471 on 17 and 184 DF, p-value: 0.9631
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MMP8_rank
Effect size...............: 9.945719
Standard error............: 20.65085
Odds ratio (effect size)..: 20862.71
Lower 95% CI..............: 0
Upper 95% CI..............: 7.901869e+21
T-value...................: 0.481613
P-value...................: 0.6306532
R^2.......................: 0.041704
Adjusted r^2..............: -0.046834
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MMP9_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
201.1 30.0
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-273.42 -128.36 -65.20 32.59 2028.74
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -664.33751 821.91836 -0.808 0.420
currentDF[, TRAIT] 32.54947 20.26581 1.606 0.110
Age 1.45634 2.83339 0.514 0.608
Gendermale 46.58506 47.63064 0.978 0.329
ORdate_epoch 0.05525 0.05443 1.015 0.311
Hypertension.compositeyes 48.93620 64.90169 0.754 0.452
DiabetesStatusDiabetes -65.45119 52.28741 -1.252 0.212
SmokerStatusEx-smoker -37.95686 46.81684 -0.811 0.419
SmokerStatusNever smoked -51.40366 61.58268 -0.835 0.405
Med.Statin.LLDyes 5.01770 46.64347 0.108 0.914
Med.all.antiplateletyes -54.41258 73.19086 -0.743 0.458
GFR_MDRD -0.77069 1.09406 -0.704 0.482
BMI -0.84285 5.39218 -0.156 0.876
MedHx_CVDNo 49.43915 43.13247 1.146 0.253
stenose50-70% 91.20286 330.98423 0.276 0.783
stenose70-90% 152.72448 287.86685 0.531 0.596
stenose90-99% 133.25998 286.60278 0.465 0.643
stenose100% (Occlusion) 251.74081 361.38764 0.697 0.487
Residual standard error: 278.5 on 184 degrees of freedom
Multiple R-squared: 0.05376, Adjusted R-squared: -0.03366
F-statistic: 0.615 on 17 and 184 DF, p-value: 0.8777
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A1
Trait/outcome.............: MMP9_rank
Effect size...............: 32.54948
Standard error............: 20.26581
Odds ratio (effect size)..: 1.367909e+14
Lower 95% CI..............: 0.001
Upper 95% CI..............: 2.435944e+31
T-value...................: 1.606127
P-value...................: 0.1099608
R^2.......................: 0.053762
Adjusted r^2..............: -0.033662
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
Analysis of COL4A2.
- processing IL2_rankfilter: removed 459 rows (74%), 163 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ stenose, data = currentDF)
Coefficients:
(Intercept) stenose70-90% stenose90-99% stenose100% (Occlusion)
225.00 309.49 62.92 1512.00
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1681.7 -300.1 -123.1 43.3 7218.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2501.6125 2628.8530 -0.952 0.3429
currentDF[, TRAIT] -33.0799 67.9392 -0.487 0.6271
Age 4.8097 9.0747 0.530 0.5969
Gendermale 149.2334 163.2505 0.914 0.3622
ORdate_epoch 0.1752 0.1875 0.934 0.3517
Hypertension.compositeyes 10.8280 212.5504 0.051 0.9594
DiabetesStatusDiabetes -49.5250 178.1017 -0.278 0.7814
SmokerStatusEx-smoker 47.3802 152.6352 0.310 0.7567
SmokerStatusNever smoked 53.5591 207.8216 0.258 0.7970
Med.Statin.LLDyes 12.1729 149.9420 0.081 0.9354
Med.all.antiplateletyes 105.9194 237.6000 0.446 0.6564
GFR_MDRD 0.1297 3.8992 0.033 0.9735
BMI 0.2824 20.2305 0.014 0.9889
MedHx_CVDNo -69.8939 141.8589 -0.493 0.6230
stenose70-90% 309.4779 606.9704 0.510 0.6109
stenose90-99% 31.2490 603.0835 0.052 0.9587
stenose100% (Occlusion) 1647.7446 878.4030 1.876 0.0627 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 809.5 on 146 degrees of freedom
Multiple R-squared: 0.08273, Adjusted R-squared: -0.01779
F-statistic: 0.823 on 16 and 146 DF, p-value: 0.658
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL2_rank
Effect size...............: -33.07994
Standard error............: 67.93922
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 2.914711e+43
T-value...................: -0.486905
P-value...................: 0.6270559
R^2.......................: 0.082729
Adjusted r^2..............: -0.017793
Sample size of AE DB......: 622
Sample size of model......: 163
Missing data %............: 73.79421
- processing IL4_rankfilter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-4324.9485 0.3853
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1376.3 -393.9 -202.2 91.4 7460.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4040.5964 3824.8141 -1.056 0.293
currentDF[, TRAIT] -9.7107 98.7492 -0.098 0.922
Age 5.8298 12.9428 0.450 0.653
Gendermale 321.5479 231.1930 1.391 0.167
ORdate_epoch 0.3514 0.2744 1.281 0.203
Hypertension.compositeyes 128.2265 303.6725 0.422 0.674
DiabetesStatusDiabetes -220.6529 252.5974 -0.874 0.384
SmokerStatusEx-smoker -129.9907 210.7223 -0.617 0.538
SmokerStatusNever smoked -106.6742 299.4329 -0.356 0.722
Med.Statin.LLDyes 69.1156 221.0163 0.313 0.755
Med.all.antiplateletyes 89.7512 331.7409 0.271 0.787
GFR_MDRD -6.0817 6.0269 -1.009 0.315
BMI -10.5330 30.1829 -0.349 0.728
MedHx_CVDNo 149.9160 201.0947 0.745 0.457
stenose70-90% 227.4481 818.0985 0.278 0.781
stenose90-99% -99.8655 811.5614 -0.123 0.902
stenose100% (Occlusion) 1461.8167 1185.7937 1.233 0.220
Residual standard error: 1078 on 128 degrees of freedom
Multiple R-squared: 0.08954, Adjusted R-squared: -0.02427
F-statistic: 0.7868 on 16 and 128 DF, p-value: 0.6981
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL4_rank
Effect size...............: -9.710658
Standard error............: 98.74917
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 6.913609e+79
T-value...................: -0.098337
P-value...................: 0.9218188
R^2.......................: 0.08954
Adjusted r^2..............: -0.024268
Sample size of AE DB......: 622
Sample size of model......: 145
Missing data %............: 76.6881
- processing IL5_rankfilter: removed 464 rows (75%), 158 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-4031.1765 0.3607
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1419.0 -385.9 -194.0 60.4 7549.1
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3697.5807 3478.6394 -1.063 0.290
currentDF[, TRAIT] -48.0210 88.6924 -0.541 0.589
Age 8.0039 12.2757 0.652 0.515
Gendermale 285.3810 214.0887 1.333 0.185
ORdate_epoch 0.2773 0.2538 1.093 0.276
Hypertension.compositeyes 45.4001 283.6855 0.160 0.873
DiabetesStatusDiabetes -149.0223 230.6014 -0.646 0.519
SmokerStatusEx-smoker -157.6038 195.0234 -0.808 0.420
SmokerStatusNever smoked -139.5908 281.6488 -0.496 0.621
Med.Statin.LLDyes 117.6645 200.6396 0.586 0.559
Med.all.antiplateletyes 151.9112 310.7940 0.489 0.626
GFR_MDRD -3.0049 5.3296 -0.564 0.574
BMI -8.1261 26.4579 -0.307 0.759
MedHx_CVDNo 129.3973 189.2517 0.684 0.495
stenose70-90% 345.4986 655.0564 0.527 0.599
stenose90-99% 84.6892 647.0581 0.131 0.896
stenose100% (Occlusion) 1634.7890 1045.7467 1.563 0.120
Residual standard error: 1048 on 141 degrees of freedom
Multiple R-squared: 0.07785, Adjusted R-squared: -0.02679
F-statistic: 0.744 on 16 and 141 DF, p-value: 0.7449
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL5_rank
Effect size...............: -48.02103
Standard error............: 88.69235
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 4.377116e+54
T-value...................: -0.541434
P-value...................: 0.5890632
R^2.......................: 0.077853
Adjusted r^2..............: -0.026788
Sample size of AE DB......: 622
Sample size of model......: 158
Missing data %............: 74.59807
- processing IL6_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
427.3
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1467.0 -362.5 -181.0 9.9 7652.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3208.2820 3472.0149 -0.924 0.357
currentDF[, TRAIT] -5.9975 83.2065 -0.072 0.943
Age 7.5599 12.1620 0.622 0.535
Gendermale 253.0957 200.1601 1.264 0.208
ORdate_epoch 0.1788 0.2313 0.773 0.441
Hypertension.compositeyes 27.1787 267.4361 0.102 0.919
DiabetesStatusDiabetes -210.4835 223.7633 -0.941 0.348
SmokerStatusEx-smoker -91.9170 191.9707 -0.479 0.633
SmokerStatusNever smoked -125.9001 266.3449 -0.473 0.637
Med.Statin.LLDyes 134.1203 189.2135 0.709 0.480
Med.all.antiplateletyes 116.0371 306.8386 0.378 0.706
GFR_MDRD -3.0664 5.1714 -0.593 0.554
BMI 3.1478 22.1815 0.142 0.887
MedHx_CVDNo 138.7382 180.4067 0.769 0.443
stenose50-70% 481.3898 1234.2421 0.390 0.697
stenose70-90% 791.1215 1072.3208 0.738 0.462
stenose90-99% 579.3810 1070.3555 0.541 0.589
stenose100% (Occlusion) 2175.1721 1351.1516 1.610 0.110
Residual standard error: 1029 on 146 degrees of freedom
Multiple R-squared: 0.0646, Adjusted R-squared: -0.04432
F-statistic: 0.5931 on 17 and 146 DF, p-value: 0.8931
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL6_rank
Effect size...............: -5.997544
Standard error............: 83.20645
Odds ratio (effect size)..: 0.002
Lower 95% CI..............: 0
Upper 95% CI..............: 1.667492e+68
T-value...................: -0.07208
P-value...................: 0.9426367
R^2.......................: 0.064597
Adjusted r^2..............: -0.04432
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing IL8_rankfilter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD + stenose, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
355.326 -4.939 306.338 378.294 340.667 3541.356
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-610.2 -306.3 -126.0 72.1 7689.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2649.3536 2894.2286 -0.915 0.36161
currentDF[, TRAIT] -24.2152 69.9452 -0.346 0.72973
Age -3.8034 9.4662 -0.402 0.68847
Gendermale 156.2782 169.0244 0.925 0.35682
ORdate_epoch 0.2377 0.1926 1.234 0.21930
Hypertension.compositeyes -140.3933 209.6624 -0.670 0.50424
DiabetesStatusDiabetes -150.3669 177.6069 -0.847 0.39869
SmokerStatusEx-smoker -36.0349 158.6794 -0.227 0.82069
SmokerStatusNever smoked -92.5953 228.3700 -0.405 0.68578
Med.Statin.LLDyes 29.6508 151.9579 0.195 0.84559
Med.all.antiplateletyes 253.2617 234.1191 1.082 0.28127
GFR_MDRD -6.3144 3.7443 -1.686 0.09401 .
BMI -3.2283 17.8906 -0.180 0.85707
MedHx_CVDNo 120.0848 148.1224 0.811 0.41895
stenose50-70% 451.8595 969.9950 0.466 0.64208
stenose70-90% 623.4309 847.1267 0.736 0.46304
stenose90-99% 595.2435 844.0454 0.705 0.48188
stenose100% (Occlusion) 3763.4335 1205.0194 3.123 0.00219 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 809.7 on 136 degrees of freedom
Multiple R-squared: 0.1428, Adjusted R-squared: 0.03564
F-statistic: 1.333 on 17 and 136 DF, p-value: 0.1816
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL8_rank
Effect size...............: -24.21521
Standard error............: 69.94522
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 1.052064e+49
T-value...................: -0.346202
P-value...................: 0.7297256
R^2.......................: 0.142792
Adjusted r^2..............: 0.03564
Sample size of AE DB......: 622
Sample size of model......: 154
Missing data %............: 75.24116
- processing IL9_rankfilter: removed 436 rows (70%), 186 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) MedHx_CVDNo
360.1 228.0
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1185.4 -355.3 -169.9 90.9 7241.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4724.665 3113.410 -1.518 0.1310
currentDF[, TRAIT] 126.809 78.403 1.617 0.1077
Age 16.429 11.411 1.440 0.1518
Gendermale 238.546 189.157 1.261 0.2090
ORdate_epoch 0.225 0.204 1.103 0.2716
Hypertension.compositeyes 243.512 255.410 0.953 0.3418
DiabetesStatusDiabetes -233.128 211.409 -1.103 0.2717
SmokerStatusEx-smoker -280.450 184.791 -1.518 0.1310
SmokerStatusNever smoked -362.908 235.309 -1.542 0.1249
Med.Statin.LLDyes 236.002 186.855 1.263 0.2083
Med.all.antiplateletyes 87.301 308.911 0.283 0.7778
GFR_MDRD -1.170 4.299 -0.272 0.7858
BMI 3.942 20.815 0.189 0.8500
MedHx_CVDNo 271.008 170.796 1.587 0.1145
stenose50-70% 350.835 1329.312 0.264 0.7922
stenose70-90% 800.848 1077.246 0.743 0.4583
stenose90-99% 611.354 1072.708 0.570 0.5695
stenose100% (Occlusion) 2433.342 1364.513 1.783 0.0763 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1042 on 168 degrees of freedom
Multiple R-squared: 0.09611, Adjusted R-squared: 0.004649
F-statistic: 1.051 on 17 and 168 DF, p-value: 0.4066
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL9_rank
Effect size...............: 126.8092
Standard error............: 78.40329
Odds ratio (effect size)..: 1.181802e+55
Lower 95% CI..............: 0
Upper 95% CI..............: 6.468027e+121
T-value...................: 1.617397
P-value...................: 0.1076687
R^2.......................: 0.096114
Adjusted r^2..............: 0.004649
Sample size of AE DB......: 622
Sample size of model......: 186
Missing data %............: 70.09646
- processing IL10_rankfilter: removed 483 rows (78%), 139 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ stenose, data = currentDF)
Coefficients:
(Intercept) stenose70-90% stenose90-99% stenose100% (Occlusion)
225.00 373.82 34.03 1512.00
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1633.9 -318.8 -134.7 84.8 7101.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3936.3327 3226.9374 -1.220 0.2249
currentDF[, TRAIT] 29.2530 78.3503 0.373 0.7095
Age 7.0478 10.5281 0.669 0.5045
Gendermale 199.8737 185.5494 1.077 0.2835
ORdate_epoch 0.2594 0.2258 1.149 0.2530
Hypertension.compositeyes 13.4826 245.3179 0.055 0.9563
DiabetesStatusDiabetes -166.7983 202.6543 -0.823 0.4121
SmokerStatusEx-smoker 38.9089 171.2913 0.227 0.8207
SmokerStatusNever smoked 56.9488 238.3795 0.239 0.8116
Med.Statin.LLDyes 42.1924 170.8276 0.247 0.8053
Med.all.antiplateletyes 125.5794 265.5669 0.473 0.6371
GFR_MDRD -0.5195 4.8689 -0.107 0.9152
BMI 7.7115 24.0529 0.321 0.7491
MedHx_CVDNo -34.1390 168.0457 -0.203 0.8394
stenose70-90% 403.7347 648.1618 0.623 0.5345
stenose90-99% 35.8665 642.9860 0.056 0.9556
stenose100% (Occlusion) 1747.3279 940.4624 1.858 0.0656 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 851.5 on 122 degrees of freedom
Multiple R-squared: 0.1074, Adjusted R-squared: -0.009713
F-statistic: 0.917 on 16 and 122 DF, p-value: 0.5516
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL10_rank
Effect size...............: 29.25298
Standard error............: 78.35031
Odds ratio (effect size)..: 5.062981e+12
Lower 95% CI..............: 0
Upper 95% CI..............: 2.49767e+79
T-value...................: 0.373361
P-value...................: 0.7095275
R^2.......................: 0.107355
Adjusted r^2..............: -0.009713
Sample size of AE DB......: 622
Sample size of model......: 139
Missing data %............: 77.65273
- processing IL12_rankfilter: removed 476 rows (77%), 146 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + stenose, data = currentDF)
Coefficients:
(Intercept) Gendermale stenose70-90% stenose90-99% stenose100% (Occlusion)
-68.96 293.96 456.53 204.34 3216.00
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-834.2 -417.5 -205.3 22.9 7578.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2722.8744 3968.6535 -0.686 0.4939
currentDF[, TRAIT] -60.3070 96.5485 -0.625 0.5333
Age 8.0539 13.3194 0.605 0.5465
Gendermale 303.6783 228.3443 1.330 0.1859
ORdate_epoch 0.2269 0.2763 0.821 0.4132
Hypertension.compositeyes -17.4399 310.5712 -0.056 0.9553
DiabetesStatusDiabetes -164.0281 247.4328 -0.663 0.5086
SmokerStatusEx-smoker -144.0609 219.5895 -0.656 0.5130
SmokerStatusNever smoked -111.8267 291.3739 -0.384 0.7018
Med.Statin.LLDyes 41.4184 218.5677 0.189 0.8500
Med.all.antiplateletyes 152.5472 332.1907 0.459 0.6469
GFR_MDRD -2.8861 5.8791 -0.491 0.6243
BMI -13.0590 29.7487 -0.439 0.6614
MedHx_CVDNo 29.9489 200.4019 0.149 0.8814
stenose70-90% 269.3055 813.4363 0.331 0.7411
stenose90-99% -26.0662 810.1864 -0.032 0.9744
stenose100% (Occlusion) 3046.0734 1425.2712 2.137 0.0345 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1074 on 129 degrees of freedom
Multiple R-squared: 0.1061, Adjusted R-squared: -0.00474
F-statistic: 0.9572 on 16 and 129 DF, p-value: 0.507
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL12_rank
Effect size...............: -60.307
Standard error............: 96.54849
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 9.834157e+55
T-value...................: -0.624629
P-value...................: 0.5333177
R^2.......................: 0.106128
Adjusted r^2..............: -0.00474
Sample size of AE DB......: 622
Sample size of model......: 146
Missing data %............: 76.52733
- processing IL13_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
444.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1271.3 -407.7 -185.6 93.5 7214.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4655.3054 3007.3924 -1.548 0.1234
currentDF[, TRAIT] 143.3603 77.4742 1.850 0.0659 .
Age 20.0666 10.6950 1.876 0.0622 .
Gendermale 244.2975 179.3815 1.362 0.1749
ORdate_epoch 0.2062 0.1995 1.034 0.3027
Hypertension.compositeyes 128.8749 234.2430 0.550 0.5829
DiabetesStatusDiabetes -161.5251 193.9284 -0.833 0.4060
SmokerStatusEx-smoker -287.6631 174.9650 -1.644 0.1019
SmokerStatusNever smoked -376.6892 228.9869 -1.645 0.1017
Med.Statin.LLDyes 166.9790 173.9497 0.960 0.3384
Med.all.antiplateletyes 116.3788 270.8672 0.430 0.6680
GFR_MDRD 0.2319 4.1465 0.056 0.9555
BMI 7.3970 19.4089 0.381 0.7036
MedHx_CVDNo 214.3950 158.5741 1.352 0.1780
stenose50-70% 290.6668 1229.2260 0.236 0.8133
stenose70-90% 675.3959 1064.1709 0.635 0.5264
stenose90-99% 470.4055 1062.9751 0.443 0.6586
stenose100% (Occlusion) 2317.2603 1332.6294 1.739 0.0837 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1029 on 184 degrees of freedom
Multiple R-squared: 0.08783, Adjusted R-squared: 0.003554
F-statistic: 1.042 on 17 and 184 DF, p-value: 0.4149
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL13_rank
Effect size...............: 143.3603
Standard error............: 77.4742
Odds ratio (effect size)..: 1.822162e+62
Lower 95% CI..............: 0
Upper 95% CI..............: 1.614199e+128
T-value...................: 1.850426
P-value...................: 0.06585545
R^2.......................: 0.087831
Adjusted r^2..............: 0.003554
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing IL21_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
444.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1275.3 -397.1 -219.6 76.9 7330.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4766.2965 3024.4945 -1.576 0.1168
currentDF[, TRAIT] 92.2995 77.2788 1.194 0.2339
Age 18.0083 10.6568 1.690 0.0928 .
Gendermale 251.9890 180.6751 1.395 0.1648
ORdate_epoch 0.2247 0.2002 1.122 0.2632
Hypertension.compositeyes 105.1841 234.9072 0.448 0.6548
DiabetesStatusDiabetes -177.0530 194.6910 -0.909 0.3643
SmokerStatusEx-smoker -260.4402 174.8468 -1.490 0.1381
SmokerStatusNever smoked -340.5608 229.2075 -1.486 0.1390
Med.Statin.LLDyes 172.1672 175.1532 0.983 0.3269
Med.all.antiplateletyes 117.8396 272.4860 0.432 0.6659
GFR_MDRD -0.0426 4.1657 -0.010 0.9919
BMI 6.2352 19.4967 0.320 0.7495
MedHx_CVDNo 205.9401 159.3268 1.293 0.1978
stenose50-70% 386.6569 1234.3726 0.313 0.7545
stenose70-90% 734.2594 1069.3727 0.687 0.4932
stenose90-99% 547.0274 1068.1134 0.512 0.6092
stenose100% (Occlusion) 2284.7701 1340.0028 1.705 0.0899 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1035 on 184 degrees of freedom
Multiple R-squared: 0.078, Adjusted R-squared: -0.00718
F-statistic: 0.9157 on 17 and 184 DF, p-value: 0.5564
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IL21_rank
Effect size...............: 92.2995
Standard error............: 77.27879
Odds ratio (effect size)..: 1.216646e+40
Lower 95% CI..............: 0
Upper 95% CI..............: 7.348431e+105
T-value...................: 1.19437
P-value...................: 0.2338705
R^2.......................: 0.078004
Adjusted r^2..............: -0.00718
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing INFG_rankfilter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ stenose, data = currentDF)
Coefficients:
(Intercept) stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
83.0 114.0 480.8 289.4 3358.0
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-777.8 -381.6 -195.2 64.6 7567.1
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5077.9708 3779.2762 -1.344 0.1813
currentDF[, TRAIT] 82.3762 97.8521 0.842 0.4014
Age 13.2264 12.4570 1.062 0.2902
Gendermale 320.6863 227.3596 1.410 0.1607
ORdate_epoch 0.2992 0.2557 1.170 0.2440
Hypertension.compositeyes -41.4073 301.1394 -0.138 0.8908
DiabetesStatusDiabetes -191.2547 228.6785 -0.836 0.4044
SmokerStatusEx-smoker -126.9641 205.9636 -0.616 0.5386
SmokerStatusNever smoked -105.6481 279.9940 -0.377 0.7065
Med.Statin.LLDyes 136.3332 203.5934 0.670 0.5042
Med.all.antiplateletyes 235.8359 298.4149 0.790 0.4307
GFR_MDRD -3.1246 5.0333 -0.621 0.5358
BMI 2.0256 23.2533 0.087 0.9307
MedHx_CVDNo 142.1832 196.2928 0.724 0.4701
stenose50-70% 440.0374 1260.1605 0.349 0.7275
stenose70-90% 787.1682 1099.3906 0.716 0.4752
stenose90-99% 528.9281 1093.2896 0.484 0.6293
stenose100% (Occlusion) 3746.4171 1553.7354 2.411 0.0172 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1049 on 136 degrees of freedom
Multiple R-squared: 0.106, Adjusted R-squared: -0.005759
F-statistic: 0.9485 on 17 and 136 DF, p-value: 0.5195
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: INFG_rank
Effect size...............: 82.37622
Standard error............: 97.85213
Odds ratio (effect size)..: 5.963997e+35
Lower 95% CI..............: 0
Upper 95% CI..............: 1.172071e+119
T-value...................: 0.841844
P-value...................: 0.4013532
R^2.......................: 0.105992
Adjusted r^2..............: -0.005759
Sample size of AE DB......: 622
Sample size of model......: 154
Missing data %............: 75.24116
- processing TNFA_rankfilter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ stenose, data = currentDF)
Coefficients:
(Intercept) stenose70-90% stenose90-99% stenose100% (Occlusion)
225.0 359.6 136.0 3216.0
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-972.0 -395.6 -194.4 42.9 7544.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2170.2974 3798.2421 -0.571 0.5687
currentDF[, TRAIT] -72.9616 95.5175 -0.764 0.4464
Age 6.6590 12.9033 0.516 0.6067
Gendermale 289.5837 225.3926 1.285 0.2012
ORdate_epoch 0.2049 0.2647 0.774 0.4402
Hypertension.compositeyes -7.5794 302.0798 -0.025 0.9800
DiabetesStatusDiabetes -208.4305 243.9177 -0.855 0.3944
SmokerStatusEx-smoker -17.3869 210.2897 -0.083 0.9342
SmokerStatusNever smoked -93.7782 291.2743 -0.322 0.7480
Med.Statin.LLDyes 15.4172 216.6515 0.071 0.9434
Med.all.antiplateletyes 148.7800 325.1606 0.458 0.6480
GFR_MDRD -2.6680 5.6028 -0.476 0.6347
BMI -22.4468 27.7475 -0.809 0.4200
MedHx_CVDNo 104.6539 203.9283 0.513 0.6087
stenose70-90% 273.7974 805.6030 0.340 0.7345
stenose90-99% -40.9069 797.4211 -0.051 0.9592
stenose100% (Occlusion) 2980.6820 1405.0428 2.121 0.0358 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1065 on 128 degrees of freedom
Multiple R-squared: 0.1131, Adjusted R-squared: 0.00221
F-statistic: 1.02 on 16 and 128 DF, p-value: 0.4404
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: TNFA_rank
Effect size...............: -72.96163
Standard error............: 95.51748
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 4.161891e+49
T-value...................: -0.763856
P-value...................: 0.4463586
R^2.......................: 0.113075
Adjusted r^2..............: 0.00221
Sample size of AE DB......: 622
Sample size of model......: 145
Missing data %............: 76.6881
- processing MIF_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
444.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1330.4 -376.0 -208.3 73.7 7493.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4885.6128 3284.3855 -1.488 0.1386
currentDF[, TRAIT] -9.4424 89.9151 -0.105 0.9165
Age 15.8951 10.5985 1.500 0.1354
Gendermale 277.6973 180.3891 1.539 0.1254
ORdate_epoch 0.2341 0.2248 1.042 0.2989
Hypertension.compositeyes 89.1227 237.1576 0.376 0.7075
DiabetesStatusDiabetes -195.6047 195.3229 -1.001 0.3179
SmokerStatusEx-smoker -237.8742 174.8202 -1.361 0.1753
SmokerStatusNever smoked -289.6426 227.5279 -1.273 0.2046
Med.Statin.LLDyes 193.1387 175.2195 1.102 0.2718
Med.all.antiplateletyes 136.2547 274.1882 0.497 0.6198
GFR_MDRD -0.1407 4.2431 -0.033 0.9736
BMI 5.7938 19.5754 0.296 0.7676
MedHx_CVDNo 195.3036 160.0366 1.220 0.2239
stenose50-70% 517.9147 1238.0878 0.418 0.6762
stenose70-90% 827.3766 1075.2543 0.769 0.4426
stenose90-99% 680.4576 1073.0365 0.634 0.5268
stenose100% (Occlusion) 2320.3393 1346.5015 1.723 0.0865 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1039 on 184 degrees of freedom
Multiple R-squared: 0.07091, Adjusted R-squared: -0.01493
F-statistic: 0.8261 on 17 and 184 DF, p-value: 0.6616
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MIF_rank
Effect size...............: -9.442425
Standard error............: 89.91506
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 2.731807e+72
T-value...................: -0.105015
P-value...................: 0.9164783
R^2.......................: 0.070912
Adjusted r^2..............: -0.014928
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MCP1_rankfilter: removed 422 rows (68%), 200 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
447.6
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1314.4 -375.3 -201.1 56.3 7508.2
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.875e+03 3.134e+03 -1.555 0.122
currentDF[, TRAIT] -1.096e+01 7.716e+01 -0.142 0.887
Age 1.618e+01 1.077e+01 1.502 0.135
Gendermale 2.726e+02 1.837e+02 1.484 0.139
ORdate_epoch 2.353e-01 2.074e-01 1.134 0.258
Hypertension.compositeyes 1.025e+02 2.389e+02 0.429 0.668
DiabetesStatusDiabetes -2.001e+02 1.970e+02 -1.015 0.311
SmokerStatusEx-smoker -2.365e+02 1.759e+02 -1.345 0.180
SmokerStatusNever smoked -2.948e+02 2.303e+02 -1.280 0.202
Med.Statin.LLDyes 1.919e+02 1.770e+02 1.084 0.280
Med.all.antiplateletyes 9.804e+01 2.916e+02 0.336 0.737
GFR_MDRD 4.906e-02 4.224e+00 0.012 0.991
BMI 5.236e+00 1.999e+01 0.262 0.794
MedHx_CVDNo 1.988e+02 1.617e+02 1.229 0.221
stenose50-70% 5.114e+02 1.241e+03 0.412 0.681
stenose70-90% 8.186e+02 1.077e+03 0.760 0.448
stenose90-99% 6.669e+02 1.073e+03 0.621 0.535
stenose100% (Occlusion) 2.269e+03 1.356e+03 1.673 0.096 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1044 on 182 degrees of freedom
Multiple R-squared: 0.07094, Adjusted R-squared: -0.01584
F-statistic: 0.8174 on 17 and 182 DF, p-value: 0.6717
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MCP1_rank
Effect size...............: -10.96447
Standard error............: 77.15834
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 8.254522e+60
T-value...................: -0.142104
P-value...................: 0.8871554
R^2.......................: 0.070937
Adjusted r^2..............: -0.015843
Sample size of AE DB......: 622
Sample size of model......: 200
Missing data %............: 67.84566
- processing MIP1a_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
451.1
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1290.9 -392.5 -185.5 85.5 7407.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4359.4113 3149.7601 -1.384 0.168
currentDF[, TRAIT] 37.8969 80.8660 0.469 0.640
Age 15.3290 11.2091 1.368 0.173
Gendermale 272.7126 193.6671 1.408 0.161
ORdate_epoch 0.2007 0.2078 0.966 0.336
Hypertension.compositeyes 115.7298 253.6959 0.456 0.649
DiabetesStatusDiabetes -255.0180 209.1480 -1.219 0.224
SmokerStatusEx-smoker -230.9273 187.4271 -1.232 0.220
SmokerStatusNever smoked -325.1301 240.8216 -1.350 0.179
Med.Statin.LLDyes 187.1791 187.0813 1.001 0.318
Med.all.antiplateletyes 118.1858 314.0445 0.376 0.707
GFR_MDRD -0.5637 4.3727 -0.129 0.898
BMI 6.9198 20.7072 0.334 0.739
MedHx_CVDNo 239.7767 173.6540 1.381 0.169
stenose50-70% 415.6587 1361.9042 0.305 0.761
stenose70-90% 805.9705 1102.8193 0.731 0.466
stenose90-99% 592.5575 1101.0252 0.538 0.591
stenose100% (Occlusion) 2251.2464 1388.6008 1.621 0.107
Residual standard error: 1063 on 171 degrees of freedom
Multiple R-squared: 0.07613, Adjusted R-squared: -0.01572
F-statistic: 0.8289 on 17 and 171 DF, p-value: 0.6582
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MIP1a_rank
Effect size...............: 37.89693
Standard error............: 80.86602
Odds ratio (effect size)..: 2.873608e+16
Lower 95% CI..............: 0
Upper 95% CI..............: 1.963224e+85
T-value...................: 0.468639
P-value...................: 0.6399249
R^2.......................: 0.07613
Adjusted r^2..............: -0.015716
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing RANTES_rankfilter: removed 424 rows (68%), 198 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
450.2
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1355.1 -381.3 -206.7 65.6 7389.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5997.4559 3255.0275 -1.843 0.0670 .
currentDF[, TRAIT] 81.4031 85.4057 0.953 0.3418
Age 18.2564 10.8700 1.680 0.0948 .
Gendermale 266.9110 185.9713 1.435 0.1530
ORdate_epoch 0.3135 0.2194 1.428 0.1549
Hypertension.compositeyes 129.3402 246.6397 0.524 0.6006
DiabetesStatusDiabetes -163.0162 201.0730 -0.811 0.4186
SmokerStatusEx-smoker -238.6119 179.4048 -1.330 0.1852
SmokerStatusNever smoked -332.6214 232.0024 -1.434 0.1534
Med.Statin.LLDyes 200.1896 177.9898 1.125 0.2622
Med.all.antiplateletyes 119.8139 290.3458 0.413 0.6803
GFR_MDRD 0.3443 4.2430 0.081 0.9354
BMI 4.9205 20.0414 0.246 0.8063
MedHx_CVDNo 238.5466 166.4211 1.433 0.1535
stenose50-70% 447.8425 1327.9225 0.337 0.7363
stenose70-90% 750.5235 1083.3165 0.693 0.4893
stenose90-99% 594.4201 1077.8441 0.551 0.5820
stenose100% (Occlusion) 2208.0641 1366.2118 1.616 0.1078
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1046 on 180 degrees of freedom
Multiple R-squared: 0.07774, Adjusted R-squared: -0.009359
F-statistic: 0.8926 on 17 and 180 DF, p-value: 0.5836
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: RANTES_rank
Effect size...............: 81.4031
Standard error............: 85.40575
Odds ratio (effect size)..: 2.253806e+35
Lower 95% CI..............: 0
Upper 95% CI..............: 1.126569e+108
T-value...................: 0.953134
P-value...................: 0.3418005
R^2.......................: 0.077743
Adjusted r^2..............: -0.009359
Sample size of AE DB......: 622
Sample size of model......: 198
Missing data %............: 68.1672
- processing MIG_rankfilter: removed 423 rows (68%), 199 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
447.1
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1294.3 -394.2 -194.9 66.1 7278.2
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3891.8813 3159.0137 -1.232 0.2195
currentDF[, TRAIT] 117.8388 84.9705 1.387 0.1672
Age 18.7096 10.8310 1.727 0.0858 .
Gendermale 245.6412 183.8225 1.336 0.1831
ORdate_epoch 0.1522 0.2125 0.717 0.4746
Hypertension.compositeyes 111.6558 243.7087 0.458 0.6474
DiabetesStatusDiabetes -172.5838 199.3592 -0.866 0.3878
SmokerStatusEx-smoker -270.9610 178.3353 -1.519 0.1304
SmokerStatusNever smoked -355.5117 233.3597 -1.523 0.1294
Med.Statin.LLDyes 174.7881 178.3630 0.980 0.3284
Med.all.antiplateletyes 128.7351 280.3506 0.459 0.6466
GFR_MDRD -0.3901 4.2122 -0.093 0.9263
BMI 6.2576 19.6713 0.318 0.7508
MedHx_CVDNo 207.1419 163.2691 1.269 0.2062
stenose50-70% 328.0648 1331.6102 0.246 0.8057
stenose70-90% 735.8283 1077.0176 0.683 0.4953
stenose90-99% 547.5523 1074.5545 0.510 0.6110
stenose100% (Occlusion) 2275.8845 1353.1786 1.682 0.0943 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1042 on 181 degrees of freedom
Multiple R-squared: 0.08024, Adjusted R-squared: -0.006141
F-statistic: 0.9289 on 17 and 181 DF, p-value: 0.5411
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MIG_rank
Effect size...............: 117.8388
Standard error............: 84.9705
Odds ratio (effect size)..: 1.502235e+51
Lower 95% CI..............: 0
Upper 95% CI..............: 3.199542e+123
T-value...................: 1.38682
P-value...................: 0.1672022
R^2.......................: 0.080245
Adjusted r^2..............: -0.006141
Sample size of AE DB......: 622
Sample size of model......: 199
Missing data %............: 68.00643
- processing IP10_rankfilter: removed 439 rows (71%), 183 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
454.7
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1401.9 -444.8 -187.2 128.7 7162.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5133.6067 3191.5957 -1.608 0.1096
currentDF[, TRAIT] 158.2081 85.4471 1.852 0.0659 .
Age 19.4200 11.6620 1.665 0.0978 .
Gendermale 314.9959 191.4456 1.645 0.1018
ORdate_epoch 0.2426 0.2100 1.155 0.2496
Hypertension.compositeyes 143.3494 259.1648 0.553 0.5809
DiabetesStatusDiabetes -242.0118 217.1440 -1.115 0.2667
SmokerStatusEx-smoker -316.1903 193.2277 -1.636 0.1037
SmokerStatusNever smoked -383.7011 248.8845 -1.542 0.1251
Med.Statin.LLDyes 185.2208 191.6020 0.967 0.3351
Med.all.antiplateletyes 145.2881 296.7351 0.490 0.6251
GFR_MDRD -1.3266 4.5560 -0.291 0.7713
BMI 8.1339 20.6342 0.394 0.6939
MedHx_CVDNo 200.3498 175.3821 1.142 0.2550
stenose50-70% 310.1383 1364.5296 0.227 0.8205
stenose70-90% 779.3861 1107.2050 0.704 0.4825
stenose90-99% 566.3439 1100.9204 0.514 0.6076
stenose100% (Occlusion) 2515.4602 1390.7539 1.809 0.0723 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1067 on 165 degrees of freedom
Multiple R-squared: 0.1, Adjusted R-squared: 0.00731
F-statistic: 1.079 on 17 and 165 DF, p-value: 0.3784
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: IP10_rank
Effect size...............: 158.2081
Standard error............: 85.44707
Odds ratio (effect size)..: 5.11581e+68
Lower 95% CI..............: 0
Upper 95% CI..............: 2.772843e+141
T-value...................: 1.851534
P-value...................: 0.06588002
R^2.......................: 0.100034
Adjusted r^2..............: 0.00731
Sample size of AE DB......: 622
Sample size of model......: 183
Missing data %............: 70.57878
- processing Eotaxin1_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
444.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1308.7 -368.3 -204.2 77.6 7468.7
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.820e+03 3.068e+03 -1.571 0.1179
currentDF[, TRAIT] 3.138e+01 7.868e+01 0.399 0.6905
Age 1.657e+01 1.066e+01 1.555 0.1217
Gendermale 2.676e+02 1.815e+02 1.474 0.1421
ORdate_epoch 2.294e-01 2.039e-01 1.125 0.2621
Hypertension.compositeyes 9.166e+01 2.355e+02 0.389 0.6975
DiabetesStatusDiabetes -1.884e+02 1.954e+02 -0.964 0.3362
SmokerStatusEx-smoker -2.437e+02 1.752e+02 -1.391 0.1660
SmokerStatusNever smoked -3.078e+02 2.298e+02 -1.339 0.1821
Med.Statin.LLDyes 1.881e+02 1.753e+02 1.073 0.2846
Med.all.antiplateletyes 1.274e+02 2.736e+02 0.466 0.6420
GFR_MDRD -2.722e-02 4.181e+00 -0.007 0.9948
BMI 6.036e+00 1.957e+01 0.308 0.7582
MedHx_CVDNo 2.003e+02 1.600e+02 1.252 0.2122
stenose50-70% 4.756e+02 1.237e+03 0.384 0.7011
stenose70-90% 7.893e+02 1.073e+03 0.736 0.4630
stenose90-99% 6.272e+02 1.072e+03 0.585 0.5592
stenose100% (Occlusion) 2.289e+03 1.346e+03 1.701 0.0907 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1039 on 184 degrees of freedom
Multiple R-squared: 0.07166, Adjusted R-squared: -0.01411
F-statistic: 0.8355 on 17 and 184 DF, p-value: 0.6507
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 31.37979
Standard error............: 78.68213
Odds ratio (effect size)..: 4.246892e+13
Lower 95% CI..............: 0
Upper 95% CI..............: 4.014661e+80
T-value...................: 0.398817
P-value...................: 0.6904903
R^2.......................: 0.071658
Adjusted r^2..............: -0.014112
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing TARC_rankfilter: removed 444 rows (71%), 178 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
483.3
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1354.6 -454.7 -185.6 116.9 7127.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6752.7641 3848.4579 -1.755 0.0812 .
currentDF[, TRAIT] 154.6719 92.1529 1.678 0.0952 .
Age 23.4276 12.1875 1.922 0.0563 .
Gendermale 346.7115 205.4402 1.688 0.0934 .
ORdate_epoch 0.3257 0.2670 1.220 0.2243
Hypertension.compositeyes 146.6537 258.9522 0.566 0.5720
DiabetesStatusDiabetes -176.8085 216.7827 -0.816 0.4159
SmokerStatusEx-smoker -265.6742 196.6755 -1.351 0.1787
SmokerStatusNever smoked -412.0578 251.3009 -1.640 0.1030
Med.Statin.LLDyes 183.4846 204.5313 0.897 0.3710
Med.all.antiplateletyes 173.4226 316.9885 0.547 0.5851
GFR_MDRD 1.2645 4.7466 0.266 0.7903
BMI 6.2787 21.9185 0.286 0.7749
MedHx_CVDNo 246.2081 180.5111 1.364 0.1745
stenose50-70% 698.3228 1385.9959 0.504 0.6151
stenose70-90% 879.8580 1132.4715 0.777 0.4383
stenose90-99% 628.9701 1128.6828 0.557 0.5781
stenose100% (Occlusion) 2475.0404 1437.2599 1.722 0.0870 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1095 on 160 degrees of freedom
Multiple R-squared: 0.09153, Adjusted R-squared: -0.004992
F-statistic: 0.9483 on 17 and 160 DF, p-value: 0.5191
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: TARC_rank
Effect size...............: 154.6719
Standard error............: 92.15287
Odds ratio (effect size)..: 1.48989e+67
Lower 95% CI..............: 0
Upper 95% CI..............: 4.123442e+145
T-value...................: 1.678427
P-value...................: 0.09521576
R^2.......................: 0.091533
Adjusted r^2..............: -0.004992
Sample size of AE DB......: 622
Sample size of model......: 178
Missing data %............: 71.38264
- processing PARC_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
444.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1380.2 -396.4 -201.2 129.2 7325.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6996.8391 3251.9937 -2.152 0.0327 *
currentDF[, TRAIT] 132.3850 82.7402 1.600 0.1113
Age 17.5095 10.5326 1.662 0.0981 .
Gendermale 286.1963 179.0315 1.599 0.1116
ORdate_epoch 0.3791 0.2158 1.756 0.0807 .
Hypertension.compositeyes 150.5961 236.7820 0.636 0.5256
DiabetesStatusDiabetes -174.5672 193.9574 -0.900 0.3693
SmokerStatusEx-smoker -247.5996 173.3082 -1.429 0.1548
SmokerStatusNever smoked -348.6743 227.6578 -1.532 0.1273
Med.Statin.LLDyes 179.7801 173.9886 1.033 0.3028
Med.all.antiplateletyes 160.1420 271.8279 0.589 0.5565
GFR_MDRD 0.7968 4.1878 0.190 0.8493
BMI 8.9721 19.5380 0.459 0.6466
MedHx_CVDNo 187.3098 158.7399 1.180 0.2395
stenose50-70% 507.3584 1226.4042 0.414 0.6796
stenose70-90% 784.8871 1064.0447 0.738 0.4617
stenose90-99% 653.6351 1060.0410 0.617 0.5383
stenose100% (Occlusion) 2479.5238 1339.7343 1.851 0.0658 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1032 on 184 degrees of freedom
Multiple R-squared: 0.08361, Adjusted R-squared: -0.001061
F-statistic: 0.9875 on 17 and 184 DF, p-value: 0.4742
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: PARC_rank
Effect size...............: 132.385
Standard error............: 82.74019
Odds ratio (effect size)..: 3.119336e+57
Lower 95% CI..............: 0
Upper 95% CI..............: 8.393316e+127
T-value...................: 1.600008
P-value...................: 0.1113124
R^2.......................: 0.083606
Adjusted r^2..............: -0.001061
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MDC_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
462.6
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1287.2 -391.7 -200.3 74.7 7440.2
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4896.9252 3270.6928 -1.497 0.1362
currentDF[, TRAIT] 13.5914 85.9735 0.158 0.8746
Age 16.6546 11.2681 1.478 0.1412
Gendermale 294.9660 193.9929 1.520 0.1302
ORdate_epoch 0.2242 0.2183 1.027 0.3059
Hypertension.compositeyes 132.5857 255.6573 0.519 0.6047
DiabetesStatusDiabetes -204.7261 212.6557 -0.963 0.3371
SmokerStatusEx-smoker -254.0823 188.0234 -1.351 0.1784
SmokerStatusNever smoked -337.9799 242.0809 -1.396 0.1645
Med.Statin.LLDyes 203.1434 190.7612 1.065 0.2884
Med.all.antiplateletyes 139.5250 317.4214 0.440 0.6608
GFR_MDRD 0.1919 4.3913 0.044 0.9652
BMI 6.7822 20.9799 0.323 0.7469
MedHx_CVDNo 226.8458 175.0531 1.296 0.1968
stenose50-70% 453.8616 1374.0253 0.330 0.7416
stenose70-90% 850.0781 1110.0615 0.766 0.4449
stenose90-99% 660.1822 1106.2831 0.597 0.5515
stenose100% (Occlusion) 2315.1944 1398.9814 1.655 0.0998 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1072 on 171 degrees of freedom
Multiple R-squared: 0.0745, Adjusted R-squared: -0.01751
F-statistic: 0.8097 on 17 and 171 DF, p-value: 0.6804
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MDC_rank
Effect size...............: 13.59145
Standard error............: 85.97347
Odds ratio (effect size)..: 799264
Lower 95% CI..............: 0
Upper 95% CI..............: 1.215587e+79
T-value...................: 0.158089
P-value...................: 0.8745733
R^2.......................: 0.074501
Adjusted r^2..............: -0.017508
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing OPG_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
441.4 107.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1421.1 -392.9 -211.5 122.6 7237.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.402e+03 3.001e+03 -1.800 0.0735 .
currentDF[, TRAIT] 1.531e+02 7.505e+01 2.040 0.0428 *
Age 1.964e+01 1.060e+01 1.853 0.0655 .
Gendermale 2.542e+02 1.785e+02 1.424 0.1562
ORdate_epoch 2.529e-01 1.981e-01 1.277 0.2033
Hypertension.compositeyes 1.327e+02 2.338e+02 0.568 0.5710
DiabetesStatusDiabetes -1.840e+02 1.928e+02 -0.954 0.3412
SmokerStatusEx-smoker -2.865e+02 1.742e+02 -1.645 0.1017
SmokerStatusNever smoked -3.552e+02 2.261e+02 -1.571 0.1179
Med.Statin.LLDyes 1.745e+02 1.733e+02 1.007 0.3153
Med.all.antiplateletyes 8.236e+01 2.713e+02 0.304 0.7618
GFR_MDRD -6.368e-02 4.135e+00 -0.015 0.9877
BMI 9.197e+00 1.942e+01 0.473 0.6365
MedHx_CVDNo 1.945e+02 1.580e+02 1.231 0.2198
stenose50-70% 4.457e+02 1.222e+03 0.365 0.7157
stenose70-90% 8.461e+02 1.059e+03 0.799 0.4255
stenose90-99% 6.701e+02 1.055e+03 0.635 0.5263
stenose100% (Occlusion) 2.527e+03 1.334e+03 1.894 0.0597 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1027 on 184 degrees of freedom
Multiple R-squared: 0.0914, Adjusted R-squared: 0.007451
F-statistic: 1.089 on 17 and 184 DF, p-value: 0.3675
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: OPG_rank
Effect size...............: 153.0667
Standard error............: 75.04838
Odds ratio (effect size)..: 2.992356e+66
Lower 95% CI..............: 392.234
Upper 95% CI..............: 2.282873e+130
T-value...................: 2.039573
P-value...................: 0.04282259
R^2.......................: 0.091397
Adjusted r^2..............: 0.007451
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing sICAM1_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
447.9 -120.7
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1291.4 -366.0 -189.4 76.1 7509.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4217.3615 3099.6176 -1.361 0.1753
currentDF[, TRAIT] -90.3037 79.5026 -1.136 0.2575
Age 13.5520 10.7416 1.262 0.2087
Gendermale 283.9378 179.6576 1.580 0.1157
ORdate_epoch 0.1871 0.2059 0.909 0.3647
Hypertension.compositeyes 63.8956 236.0369 0.271 0.7869
DiabetesStatusDiabetes -210.8814 194.7792 -1.083 0.2804
SmokerStatusEx-smoker -228.6198 173.9032 -1.315 0.1903
SmokerStatusNever smoked -246.6294 229.1579 -1.076 0.2832
Med.Statin.LLDyes 196.0482 174.4407 1.124 0.2625
Med.all.antiplateletyes 144.7690 272.4283 0.531 0.5958
GFR_MDRD -0.5267 4.1870 -0.126 0.9000
BMI 6.3807 19.5077 0.327 0.7440
MedHx_CVDNo 175.7011 160.2155 1.097 0.2742
stenose50-70% 571.4894 1231.8463 0.464 0.6432
stenose70-90% 933.0143 1072.3154 0.870 0.3854
stenose90-99% 778.6167 1068.0066 0.729 0.4669
stenose100% (Occlusion) 2400.4842 1342.4651 1.788 0.0754 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1035 on 184 degrees of freedom
Multiple R-squared: 0.07733, Adjusted R-squared: -0.007922
F-statistic: 0.9071 on 17 and 184 DF, p-value: 0.5665
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: sICAM1_rank
Effect size...............: -90.3037
Standard error............: 79.50263
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 2.854969e+28
T-value...................: -1.135858
P-value...................: 0.2574932
R^2.......................: 0.077325
Adjusted r^2..............: -0.007922
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing VEGFA_rankfilter: removed 445 rows (72%), 177 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
437.3
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1421.0 -362.9 -206.0 90.3 7677.3
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4074.3774 3130.5063 -1.302 0.1950
currentDF[, TRAIT] -52.2011 83.8197 -0.623 0.5343
Age 11.6041 10.9702 1.058 0.2917
Gendermale 315.1773 185.9953 1.695 0.0921 .
ORdate_epoch 0.2578 0.2327 1.108 0.2696
Hypertension.compositeyes 23.6338 248.5109 0.095 0.9244
DiabetesStatusDiabetes -172.5580 199.5466 -0.865 0.3885
SmokerStatusEx-smoker -125.9425 177.4109 -0.710 0.4788
SmokerStatusNever smoked -231.4688 242.9764 -0.953 0.3422
Med.Statin.LLDyes 160.3609 178.3420 0.899 0.3699
Med.all.antiplateletyes 137.5293 266.3780 0.516 0.6064
GFR_MDRD -0.5800 3.9431 -0.147 0.8833
BMI -0.6112 20.4015 -0.030 0.9761
MedHx_CVDNo 131.0385 165.6383 0.791 0.4300
stenose70-90% 277.0597 764.0882 0.363 0.7174
stenose90-99% 38.9227 756.9654 0.051 0.9591
stenose100% (Occlusion) 1596.5357 1077.6260 1.482 0.1404
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 996.2 on 160 degrees of freedom
Multiple R-squared: 0.06779, Adjusted R-squared: -0.02544
F-statistic: 0.7271 on 16 and 160 DF, p-value: 0.7634
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: VEGFA_rank
Effect size...............: -52.20106
Standard error............: 83.81972
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 4.766031e+48
T-value...................: -0.622778
P-value...................: 0.5343172
R^2.......................: 0.067785
Adjusted r^2..............: -0.025436
Sample size of AE DB......: 622
Sample size of model......: 177
Missing data %............: 71.54341
- processing TGFB_rankfilter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
448.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1320.8 -365.1 -210.3 67.8 7495.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4975.2103 3001.1090 -1.658 0.0991 .
currentDF[, TRAIT] -14.4343 76.1781 -0.189 0.8499
Age 15.8442 10.6411 1.489 0.1382
Gendermale 277.3107 178.5411 1.553 0.1221
ORdate_epoch 0.2437 0.1993 1.223 0.2229
Hypertension.compositeyes 102.7735 232.1093 0.443 0.6584
DiabetesStatusDiabetes -204.0086 189.2582 -1.078 0.2825
SmokerStatusEx-smoker -228.0073 173.8539 -1.311 0.1913
SmokerStatusNever smoked -284.8122 230.6085 -1.235 0.2184
Med.Statin.LLDyes 199.2938 174.4554 1.142 0.2548
Med.all.antiplateletyes 106.1065 280.4128 0.378 0.7056
GFR_MDRD -0.1526 4.1605 -0.037 0.9708
BMI 5.0556 19.6857 0.257 0.7976
MedHx_CVDNo 183.7086 159.2307 1.154 0.2501
stenose50-70% 551.1037 1239.6099 0.445 0.6571
stenose70-90% 837.3984 1070.6262 0.782 0.4351
stenose90-99% 688.1105 1067.0149 0.645 0.5198
stenose100% (Occlusion) 2324.5216 1353.5328 1.717 0.0876 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1036 on 185 degrees of freedom
Multiple R-squared: 0.07193, Adjusted R-squared: -0.01335
F-statistic: 0.8435 on 17 and 185 DF, p-value: 0.6413
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: TGFB_rank
Effect size...............: -14.43427
Standard error............: 76.17809
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 3.761658e+58
T-value...................: -0.189481
P-value...................: 0.8499239
R^2.......................: 0.071933
Adjusted r^2..............: -0.013349
Sample size of AE DB......: 622
Sample size of model......: 203
Missing data %............: 67.36334
- processing MMP2_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
407.1
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1519.7 -324.0 -171.5 38.6 7717.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3007.7611 2803.7242 -1.073 0.2848
currentDF[, TRAIT] 16.6203 71.5686 0.232 0.8166
Age 9.8691 9.5439 1.034 0.3025
Gendermale 229.0414 161.4428 1.419 0.1577
ORdate_epoch 0.1547 0.1851 0.836 0.4042
Hypertension.compositeyes 58.0518 218.2743 0.266 0.7906
DiabetesStatusDiabetes -131.5848 174.7963 -0.753 0.4525
SmokerStatusEx-smoker -96.9790 158.1295 -0.613 0.5404
SmokerStatusNever smoked -144.6567 208.7915 -0.693 0.4893
Med.Statin.LLDyes 125.1764 157.0460 0.797 0.4264
Med.all.antiplateletyes 102.0896 246.5816 0.414 0.6793
GFR_MDRD -1.2741 3.6585 -0.348 0.7280
BMI -0.1049 18.1936 -0.006 0.9954
MedHx_CVDNo 107.2437 144.8341 0.740 0.4600
stenose50-70% 388.8390 1116.3836 0.348 0.7280
stenose70-90% 659.4346 969.1413 0.680 0.4971
stenose90-99% 477.6694 964.9734 0.495 0.6212
stenose100% (Occlusion) 2071.4666 1217.4230 1.702 0.0905 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 938.2 on 184 degrees of freedom
Multiple R-squared: 0.05599, Adjusted R-squared: -0.03123
F-statistic: 0.6419 on 17 and 184 DF, p-value: 0.8551
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MMP2_rank
Effect size...............: 16.62032
Standard error............: 71.56859
Odds ratio (effect size)..: 16523898
Lower 95% CI..............: 0
Upper 95% CI..............: 1.37571e+68
T-value...................: 0.232229
P-value...................: 0.8166181
R^2.......................: 0.055986
Adjusted r^2..............: -0.031233
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MMP8_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
407.1
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1476.7 -336.8 -171.6 34.8 7698.1
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3093.0254 2754.3117 -1.123 0.263
currentDF[, TRAIT] -59.8434 69.0026 -0.867 0.387
Age 9.1088 9.4974 0.959 0.339
Gendermale 248.5477 162.2467 1.532 0.127
ORdate_epoch 0.1534 0.1818 0.844 0.400
Hypertension.compositeyes 50.4339 215.0734 0.234 0.815
DiabetesStatusDiabetes -149.7964 175.7979 -0.852 0.395
SmokerStatusEx-smoker -102.0318 157.6322 -0.647 0.518
SmokerStatusNever smoked -139.2726 206.5284 -0.674 0.501
Med.Statin.LLDyes 123.9846 156.7426 0.791 0.430
Med.all.antiplateletyes 95.1360 245.9113 0.387 0.699
GFR_MDRD -1.8758 3.6648 -0.512 0.609
BMI 0.9094 18.1763 0.050 0.960
MedHx_CVDNo 93.8097 145.2186 0.646 0.519
stenose50-70% 528.8410 1122.2883 0.471 0.638
stenose70-90% 849.7929 989.7314 0.859 0.392
stenose90-99% 648.4726 981.7045 0.661 0.510
stenose100% (Occlusion) 2270.9309 1236.8623 1.836 0.068 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 936.4 on 184 degrees of freedom
Multiple R-squared: 0.05955, Adjusted R-squared: -0.02734
F-statistic: 0.6854 on 17 and 184 DF, p-value: 0.8149
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MMP8_rank
Effect size...............: -59.8434
Standard error............: 69.0026
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 5.578764e+32
T-value...................: -0.867263
P-value...................: 0.3869275
R^2.......................: 0.059553
Adjusted r^2..............: -0.027336
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MMP9_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
407.1
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1519.2 -327.7 -167.5 53.9 7726.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2930.9426 2769.2601 -1.058 0.2913
currentDF[, TRAIT] 10.5011 68.2809 0.154 0.8779
Age 9.7963 9.5464 1.026 0.3062
Gendermale 222.5818 160.4802 1.387 0.1671
ORdate_epoch 0.1504 0.1834 0.820 0.4133
Hypertension.compositeyes 55.6755 218.6710 0.255 0.7993
DiabetesStatusDiabetes -127.4860 176.1701 -0.724 0.4702
SmokerStatusEx-smoker -94.8230 157.7383 -0.601 0.5485
SmokerStatusNever smoked -140.5054 207.4883 -0.677 0.4991
Med.Statin.LLDyes 123.9044 157.1542 0.788 0.4315
Med.all.antiplateletyes 103.3401 246.5994 0.419 0.6757
GFR_MDRD -1.2840 3.6862 -0.348 0.7280
BMI -0.5338 18.1677 -0.029 0.9766
MedHx_CVDNo 108.2155 145.3247 0.745 0.4574
stenose50-70% 399.0087 1115.1733 0.358 0.7209
stenose70-90% 659.1119 969.8995 0.680 0.4976
stenose90-99% 476.5197 965.6405 0.493 0.6223
stenose100% (Occlusion) 2068.3609 1217.6104 1.699 0.0911 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 938.3 on 184 degrees of freedom
Multiple R-squared: 0.05583, Adjusted R-squared: -0.0314
F-statistic: 0.64 on 17 and 184 DF, p-value: 0.8568
Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: COL4A2
Trait/outcome.............: MMP9_rank
Effect size...............: 10.50105
Standard error............: 68.28088
Odds ratio (effect size)..: 36353.85
Lower 95% CI..............: 0
Upper 95% CI..............: 4.812974e+62
T-value...................: 0.153792
P-value...................: 0.8779423
R^2.......................: 0.055831
Adjusted r^2..............: -0.031403
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
Analysis of LDLR.
- processing IL2_rankfilter: removed 459 rows (74%), 163 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + DiabetesStatus,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] DiabetesStatusDiabetes
253.40 -39.59 -97.89
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-328.09 -149.83 -58.81 78.25 3029.61
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 520.91268 1093.38784 0.476 0.634
currentDF[, TRAIT] -46.31053 28.25716 -1.639 0.103
Age -5.10282 3.77433 -1.352 0.178
Gendermale 23.38151 67.89886 0.344 0.731
ORdate_epoch 0.03022 0.07798 0.388 0.699
Hypertension.compositeyes 60.97765 88.40359 0.690 0.491
DiabetesStatusDiabetes -110.81618 74.07574 -1.496 0.137
SmokerStatusEx-smoker 30.02386 63.48378 0.473 0.637
SmokerStatusNever smoked -39.51239 86.43680 -0.457 0.648
Med.Statin.LLDyes -91.49241 62.36361 -1.467 0.145
Med.all.antiplateletyes -2.82203 98.82217 -0.029 0.977
GFR_MDRD -1.38985 1.62177 -0.857 0.393
BMI -8.84295 8.41422 -1.051 0.295
MedHx_CVDNo -25.11557 59.00172 -0.426 0.671
stenose70-90% 14.95581 252.45003 0.059 0.953
stenose90-99% 44.26366 250.83339 0.176 0.860
stenose100% (Occlusion) -82.85074 365.34379 -0.227 0.821
Residual standard error: 336.7 on 146 degrees of freedom
Multiple R-squared: 0.06159, Adjusted R-squared: -0.04125
F-statistic: 0.5989 on 16 and 146 DF, p-value: 0.8809
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL2_rank
Effect size...............: -46.31053
Standard error............: 28.25716
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 8721.072
T-value...................: -1.638896
P-value...................: 0.1033881
R^2.......................: 0.061591
Adjusted r^2..............: -0.041248
Sample size of AE DB......: 622
Sample size of model......: 163
Missing data %............: 73.79421
- processing IL4_rankfilter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
238.4
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-318.43 -156.42 -72.46 60.35 3030.71
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 62.83957 1270.88420 0.049 0.961
currentDF[, TRAIT] -28.34536 32.81173 -0.864 0.389
Age -5.27680 4.30053 -1.227 0.222
Gendermale 37.76020 76.81928 0.492 0.624
ORdate_epoch 0.06565 0.09117 0.720 0.473
Hypertension.compositeyes 83.53592 100.90231 0.828 0.409
DiabetesStatusDiabetes -108.26611 83.93141 -1.290 0.199
SmokerStatusEx-smoker 10.19777 70.01742 0.146 0.884
SmokerStatusNever smoked -27.76318 99.49359 -0.279 0.781
Med.Statin.LLDyes -119.10325 73.43785 -1.622 0.107
Med.all.antiplateletyes 14.13995 110.22870 0.128 0.898
GFR_MDRD -1.51017 2.00257 -0.754 0.452
BMI -8.09419 10.02896 -0.807 0.421
MedHx_CVDNo -38.69057 66.81842 -0.579 0.564
stenose70-90% 27.90398 271.83240 0.103 0.918
stenose90-99% 45.64903 269.66032 0.169 0.866
stenose100% (Occlusion) -57.54530 394.00776 -0.146 0.884
Residual standard error: 358.2 on 128 degrees of freedom
Multiple R-squared: 0.05576, Adjusted R-squared: -0.06227
F-statistic: 0.4724 on 16 and 128 DF, p-value: 0.9562
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL4_rank
Effect size...............: -28.34536
Standard error............: 32.81173
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 4.165559e+15
T-value...................: -0.863879
P-value...................: 0.3892703
R^2.......................: 0.055762
Adjusted r^2..............: -0.062268
Sample size of AE DB......: 622
Sample size of model......: 145
Missing data %............: 76.6881
- processing IL5_rankfilter: removed 464 rows (75%), 158 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
234.19 -49.58
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-374.28 -152.05 -54.11 58.50 3018.55
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 493.67791 1131.34306 0.436 0.6632
currentDF[, TRAIT] -62.20168 28.84504 -2.156 0.0327 *
Age -5.56773 3.99237 -1.395 0.1653
Gendermale 32.71229 69.62715 0.470 0.6392
ORdate_epoch 0.02714 0.08253 0.329 0.7427
Hypertension.compositeyes 72.34639 92.26184 0.784 0.4343
DiabetesStatusDiabetes -102.47357 74.99751 -1.366 0.1740
SmokerStatusEx-smoker 11.82359 63.42663 0.186 0.8524
SmokerStatusNever smoked -36.60120 91.59944 -0.400 0.6901
Med.Statin.LLDyes -96.29147 65.25316 -1.476 0.1423
Med.all.antiplateletyes -2.00327 101.07822 -0.020 0.9842
GFR_MDRD -0.65153 1.73332 -0.376 0.7076
BMI -7.83577 8.60479 -0.911 0.3640
MedHx_CVDNo -27.95813 61.54952 -0.454 0.6504
stenose70-90% 29.28637 213.04120 0.137 0.8909
stenose90-99% 50.26398 210.43995 0.239 0.8116
stenose100% (Occlusion) -112.98204 340.10373 -0.332 0.7402
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 340.8 on 141 degrees of freedom
Multiple R-squared: 0.0656, Adjusted R-squared: -0.04044
F-statistic: 0.6186 on 16 and 141 DF, p-value: 0.8649
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL5_rank
Effect size...............: -62.20168
Standard error............: 28.84504
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 0.003
T-value...................: -2.156408
P-value...................: 0.03274785
R^2.......................: 0.065596
Adjusted r^2..............: -0.040435
Sample size of AE DB......: 622
Sample size of model......: 158
Missing data %............: 74.59807
- processing IL6_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
235.2 -47.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-373.36 -143.85 -60.65 60.68 3035.36
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -248.62021 1140.08716 -0.218 0.828
currentDF[, TRAIT] -54.74029 27.32206 -2.004 0.047 *
Age -4.85318 3.99357 -1.215 0.226
Gendermale 13.81295 65.72550 0.210 0.834
ORdate_epoch 0.07104 0.07596 0.935 0.351
Hypertension.compositeyes 62.84548 87.81658 0.716 0.475
DiabetesStatusDiabetes -82.96563 73.47596 -1.129 0.261
SmokerStatusEx-smoker 18.25252 63.03640 0.290 0.773
SmokerStatusNever smoked -39.70686 87.45826 -0.454 0.650
Med.Statin.LLDyes -86.93234 62.13102 -1.399 0.164
Med.all.antiplateletyes -22.47669 100.75498 -0.223 0.824
GFR_MDRD -1.15257 1.69811 -0.679 0.498
BMI -3.58069 7.28361 -0.492 0.624
MedHx_CVDNo -26.49345 59.23920 -0.447 0.655
stenose50-70% 100.22164 405.28156 0.247 0.805
stenose70-90% 120.51754 352.11232 0.342 0.733
stenose90-99% 149.41896 351.46697 0.425 0.671
stenose100% (Occlusion) 68.29886 443.67050 0.154 0.878
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 337.9 on 146 degrees of freedom
Multiple R-squared: 0.05976, Adjusted R-squared: -0.04972
F-statistic: 0.5458 on 17 and 146 DF, p-value: 0.925
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL6_rank
Effect size...............: -54.74029
Standard error............: 27.32206
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 0.305
T-value...................: -2.00352
P-value...................: 0.04697195
R^2.......................: 0.059756
Adjusted r^2..............: -0.049725
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing IL8_rankfilter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
245.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-348.09 -149.23 -61.71 61.30 3070.28
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 235.68769 1251.63975 0.188 0.851
currentDF[, TRAIT] 39.24104 30.24855 1.297 0.197
Age -3.99713 4.09374 -0.976 0.331
Gendermale 6.68264 73.09637 0.091 0.927
ORdate_epoch 0.03254 0.08331 0.391 0.697
Hypertension.compositeyes 52.22622 90.67071 0.576 0.566
DiabetesStatusDiabetes -96.31304 76.80799 -1.254 0.212
SmokerStatusEx-smoker 22.74462 68.62257 0.331 0.741
SmokerStatusNever smoked -41.33647 98.76103 -0.419 0.676
Med.Statin.LLDyes -78.89004 65.71581 -1.200 0.232
Med.all.antiplateletyes 25.72044 101.24727 0.254 0.800
GFR_MDRD -2.21278 1.61926 -1.367 0.174
BMI -2.66139 7.73697 -0.344 0.731
MedHx_CVDNo -15.35188 64.05711 -0.240 0.811
stenose50-70% 112.97386 419.48461 0.269 0.788
stenose70-90% 69.64010 366.34889 0.190 0.850
stenose90-99% 113.71150 365.01635 0.312 0.756
stenose100% (Occlusion) 176.37355 521.12338 0.338 0.736
Residual standard error: 350.2 on 136 degrees of freedom
Multiple R-squared: 0.05275, Adjusted R-squared: -0.06566
F-statistic: 0.4455 on 17 and 136 DF, p-value: 0.9712
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL8_rank
Effect size...............: 39.24104
Standard error............: 30.24855
Odds ratio (effect size)..: 1.101967e+17
Lower 95% CI..............: 0
Upper 95% CI..............: 6.16955e+42
T-value...................: 1.297287
P-value...................: 0.1967289
R^2.......................: 0.052748
Adjusted r^2..............: -0.065659
Sample size of AE DB......: 622
Sample size of model......: 154
Missing data %............: 75.24116
- processing IL9_rankfilter: removed 436 rows (70%), 186 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
238.02 47.24
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-316.07 -136.86 -52.12 51.94 3054.92
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -217.20901 960.87559 -0.226 0.8214
currentDF[, TRAIT] 47.03353 24.19720 1.944 0.0536 .
Age -2.53338 3.52165 -0.719 0.4729
Gendermale 54.23655 58.37864 0.929 0.3542
ORdate_epoch 0.05125 0.06295 0.814 0.4167
Hypertension.compositeyes 88.41573 78.82601 1.122 0.2636
DiabetesStatusDiabetes -69.21586 65.24616 -1.061 0.2903
SmokerStatusEx-smoker -21.10761 57.03112 -0.370 0.7118
SmokerStatusNever smoked -71.42706 72.62220 -0.984 0.3268
Med.Statin.LLDyes -50.52447 57.66801 -0.876 0.3822
Med.all.antiplateletyes 13.28236 95.33755 0.139 0.8894
GFR_MDRD -1.58419 1.32668 -1.194 0.2341
BMI -2.77936 6.42391 -0.433 0.6658
MedHx_CVDNo -14.04654 52.71192 -0.266 0.7902
stenose50-70% 43.29069 410.25876 0.106 0.9161
stenose70-90% 110.93439 332.46474 0.334 0.7390
stenose90-99% 114.07784 331.06438 0.345 0.7308
stenose100% (Occlusion) 134.42324 421.12264 0.319 0.7500
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 321.6 on 168 degrees of freedom
Multiple R-squared: 0.06134, Adjusted R-squared: -0.03364
F-statistic: 0.6458 on 17 and 168 DF, p-value: 0.8512
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL9_rank
Effect size...............: 47.03353
Standard error............: 24.1972
Odds ratio (effect size)..: 2.669337e+20
Lower 95% CI..............: 0.675
Upper 95% CI..............: 1.055548e+41
T-value...................: 1.943759
P-value...................: 0.05359534
R^2.......................: 0.061339
Adjusted r^2..............: -0.033645
Sample size of AE DB......: 622
Sample size of model......: 186
Missing data %............: 70.09646
- processing IL10_rankfilter: removed 483 rows (78%), 139 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
236
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-405.59 -157.26 -73.03 59.17 3051.99
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 387.18086 1392.05420 0.278 0.781
currentDF[, TRAIT] -31.55853 33.79919 -0.934 0.352
Age -4.59064 4.54165 -1.011 0.314
Gendermale 34.47565 80.04331 0.431 0.667
ORdate_epoch 0.03035 0.09741 0.312 0.756
Hypertension.compositeyes 73.07362 105.82658 0.691 0.491
DiabetesStatusDiabetes -109.45375 87.42213 -1.252 0.213
SmokerStatusEx-smoker 6.96094 73.89260 0.094 0.925
SmokerStatusNever smoked -16.15678 102.83348 -0.157 0.875
Med.Statin.LLDyes -100.60239 73.69258 -1.365 0.175
Med.all.antiplateletyes 7.48007 114.56175 0.065 0.948
GFR_MDRD -1.07520 2.10036 -0.512 0.610
BMI -6.67115 10.37607 -0.643 0.521
MedHx_CVDNo -38.79323 72.49249 -0.535 0.594
stenose70-90% 18.34613 279.60763 0.066 0.948
stenose90-99% 42.19636 277.37485 0.152 0.879
stenose100% (Occlusion) -71.78452 405.70190 -0.177 0.860
Residual standard error: 367.3 on 122 degrees of freedom
Multiple R-squared: 0.04846, Adjusted R-squared: -0.07633
F-statistic: 0.3884 on 16 and 122 DF, p-value: 0.9831
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL10_rank
Effect size...............: -31.55853
Standard error............: 33.79919
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 1.160802e+15
T-value...................: -0.933707
P-value...................: 0.3522996
R^2.......................: 0.048464
Adjusted r^2..............: -0.076328
Sample size of AE DB......: 622
Sample size of model......: 139
Missing data %............: 77.65273
- processing IL12_rankfilter: removed 476 rows (77%), 146 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
241.8
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-385.06 -156.12 -62.14 62.01 3003.89
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 472.05050 1310.13762 0.360 0.7192
currentDF[, TRAIT] -39.64004 31.87273 -1.244 0.2159
Age -5.57318 4.39702 -1.267 0.2073
Gendermale 36.59613 75.38136 0.485 0.6282
ORdate_epoch 0.03843 0.09123 0.421 0.6743
Hypertension.compositeyes 59.04102 102.52622 0.576 0.5657
DiabetesStatusDiabetes -117.45838 81.68289 -1.438 0.1529
SmokerStatusEx-smoker 10.44556 72.49122 0.144 0.8857
SmokerStatusNever smoked -44.12572 96.18877 -0.459 0.6472
Med.Statin.LLDyes -121.08550 72.15387 -1.678 0.0957 .
Med.all.antiplateletyes -12.09649 109.66328 -0.110 0.9123
GFR_MDRD -1.23103 1.94082 -0.634 0.5270
BMI -8.80030 9.82068 -0.896 0.3719
MedHx_CVDNo -50.37161 66.15698 -0.761 0.4478
stenose70-90% 38.41980 268.53278 0.143 0.8865
stenose90-99% 46.61311 267.45991 0.174 0.8619
stenose100% (Occlusion) 36.25866 470.51257 0.077 0.9387
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 354.6 on 129 degrees of freedom
Multiple R-squared: 0.0616, Adjusted R-squared: -0.05479
F-statistic: 0.5293 on 16 and 129 DF, p-value: 0.9275
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL12_rank
Effect size...............: -39.64004
Standard error............: 31.87273
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 8225467033
T-value...................: -1.243698
P-value...................: 0.2158661
R^2.......................: 0.061604
Adjusted r^2..............: -0.054786
Sample size of AE DB......: 622
Sample size of model......: 146
Missing data %............: 76.52733
- processing IL13_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
241.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-316.86 -145.58 -53.09 48.01 3072.98
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -66.75464 969.38841 -0.069 0.945
currentDF[, TRAIT] 35.26933 24.97266 1.412 0.160
Age -0.66512 3.44738 -0.193 0.847
Gendermale 24.16385 57.82098 0.418 0.677
ORdate_epoch 0.04291 0.06431 0.667 0.505
Hypertension.compositeyes 37.60088 75.50477 0.498 0.619
DiabetesStatusDiabetes -77.13837 62.50994 -1.234 0.219
SmokerStatusEx-smoker -43.33149 56.39738 -0.768 0.443
SmokerStatusNever smoked -96.54963 73.81055 -1.308 0.192
Med.Statin.LLDyes -64.45092 56.07011 -1.149 0.252
Med.all.antiplateletyes -76.90992 87.31003 -0.881 0.380
GFR_MDRD -2.03679 1.33658 -1.524 0.129
BMI -0.66104 6.25617 -0.106 0.916
MedHx_CVDNo 11.65805 51.11402 0.228 0.820
stenose50-70% 57.08558 396.22282 0.144 0.886
stenose70-90% 94.29734 343.01973 0.275 0.784
stenose90-99% 80.68105 342.63429 0.235 0.814
stenose100% (Occlusion) 25.83165 429.55337 0.060 0.952
Residual standard error: 331.8 on 184 degrees of freedom
Multiple R-squared: 0.04798, Adjusted R-squared: -0.03998
F-statistic: 0.5454 on 17 and 184 DF, p-value: 0.9265
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL13_rank
Effect size...............: 35.26933
Standard error............: 24.97266
Odds ratio (effect size)..: 2.076235e+15
Lower 95% CI..............: 0
Upper 95% CI..............: 3.753488e+36
T-value...................: 1.412318
P-value...................: 0.1595455
R^2.......................: 0.047976
Adjusted r^2..............: -0.039983
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing IL21_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] GFR_MDRD
360.26 31.85 -1.70
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-331.19 -149.43 -55.93 33.13 3070.69
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -51.17276 968.62788 -0.053 0.958
currentDF[, TRAIT] 38.36944 24.74939 1.550 0.123
Age -0.82856 3.41295 -0.243 0.808
Gendermale 21.85230 57.86319 0.378 0.706
ORdate_epoch 0.04402 0.06411 0.687 0.493
Hypertension.compositeyes 34.00657 75.23164 0.452 0.652
DiabetesStatusDiabetes -78.03874 62.35196 -1.252 0.212
SmokerStatusEx-smoker -40.68773 55.99664 -0.727 0.468
SmokerStatusNever smoked -95.90473 73.40625 -1.306 0.193
Med.Statin.LLDyes -66.58186 56.09475 -1.187 0.237
Med.all.antiplateletyes -79.26136 87.26664 -0.908 0.365
GFR_MDRD -2.10049 1.33412 -1.574 0.117
BMI -0.86179 6.24405 -0.138 0.890
MedHx_CVDNo 11.20776 51.02617 0.220 0.826
stenose50-70% 60.01803 395.32151 0.152 0.879
stenose70-90% 94.64212 342.47844 0.276 0.783
stenose90-99% 78.86208 342.07516 0.231 0.818
stenose100% (Occlusion) 12.96177 429.15073 0.030 0.976
Residual standard error: 331.5 on 184 degrees of freedom
Multiple R-squared: 0.05006, Adjusted R-squared: -0.0377
F-statistic: 0.5704 on 17 and 184 DF, p-value: 0.9106
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IL21_rank
Effect size...............: 38.36944
Standard error............: 24.74939
Odds ratio (effect size)..: 4.609299e+16
Lower 95% CI..............: 0
Upper 95% CI..............: 5.379461e+37
T-value...................: 1.550319
P-value...................: 0.1227833
R^2.......................: 0.050064
Adjusted r^2..............: -0.037702
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing INFG_rankfilter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
243.6
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-355.24 -147.60 -70.37 75.36 3069.14
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 132.49024 1257.86295 0.105 0.916
currentDF[, TRAIT] -22.33873 32.56829 -0.686 0.494
Age -5.38684 4.14607 -1.299 0.196
Gendermale 22.44825 75.67250 0.297 0.767
ORdate_epoch 0.04762 0.08509 0.560 0.577
Hypertension.compositeyes 41.22299 100.22874 0.411 0.682
DiabetesStatusDiabetes -115.54334 76.11146 -1.518 0.131
SmokerStatusEx-smoker 29.12536 68.55121 0.425 0.672
SmokerStatusNever smoked -19.37587 93.19090 -0.208 0.836
Med.Statin.LLDyes -104.06856 67.76233 -1.536 0.127
Med.all.antiplateletyes 21.19570 99.32193 0.213 0.831
GFR_MDRD -1.70420 1.67524 -1.017 0.311
BMI -4.45845 7.73944 -0.576 0.566
MedHx_CVDNo -38.27717 65.33247 -0.586 0.559
stenose50-70% 119.69398 419.42136 0.285 0.776
stenose70-90% 133.62269 365.91206 0.365 0.716
stenose90-99% 151.11773 363.88144 0.415 0.679
stenose100% (Occlusion) 200.51545 517.13241 0.388 0.699
Residual standard error: 349.1 on 136 degrees of freedom
Multiple R-squared: 0.05155, Adjusted R-squared: -0.06701
F-statistic: 0.4348 on 17 and 136 DF, p-value: 0.9746
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: INFG_rank
Effect size...............: -22.33873
Standard error............: 32.56829
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 1.049788e+18
T-value...................: -0.685904
P-value...................: 0.493941
R^2.......................: 0.051545
Adjusted r^2..............: -0.067011
Sample size of AE DB......: 622
Sample size of model......: 154
Missing data %............: 75.24116
- processing TNFA_rankfilter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
239
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-365.27 -159.80 -66.71 64.14 2997.30
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 576.76393 1272.02456 0.453 0.6510
currentDF[, TRAIT] -47.42767 31.98864 -1.483 0.1406
Age -5.21971 4.32130 -1.208 0.2293
Gendermale 24.55641 75.48357 0.325 0.7455
ORdate_epoch 0.02532 0.08864 0.286 0.7756
Hypertension.compositeyes 78.30478 101.16598 0.774 0.4403
DiabetesStatusDiabetes -107.85337 81.68760 -1.320 0.1891
SmokerStatusEx-smoker 28.25752 70.42564 0.401 0.6889
SmokerStatusNever smoked -36.42460 97.54724 -0.373 0.7095
Med.Statin.LLDyes -122.13493 72.55619 -1.683 0.0948 .
Med.all.antiplateletyes -6.60513 108.89571 -0.061 0.9517
GFR_MDRD -1.12867 1.87635 -0.602 0.5486
BMI -8.74042 9.29258 -0.941 0.3487
MedHx_CVDNo -62.36279 68.29523 -0.913 0.3629
stenose70-90% 34.50883 269.79503 0.128 0.8984
stenose90-99% 53.76952 267.05490 0.201 0.8408
stenose100% (Occlusion) 70.70280 470.54635 0.150 0.8808
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 356.7 on 128 degrees of freedom
Multiple R-squared: 0.06219, Adjusted R-squared: -0.05503
F-statistic: 0.5305 on 16 and 128 DF, p-value: 0.9267
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: TNFA_rank
Effect size...............: -47.42767
Standard error............: 31.98864
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 4282526
T-value...................: -1.482641
P-value...................: 0.1406276
R^2.......................: 0.062191
Adjusted r^2..............: -0.055035
Sample size of AE DB......: 622
Sample size of model......: 145
Missing data %............: 76.6881
- processing MIF_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
241.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-359.92 -146.45 -59.04 46.75 3056.28
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -757.99845 1048.40701 -0.723 0.471
currentDF[, TRAIT] 42.58446 28.70174 1.484 0.140
Age -1.25244 3.38313 -0.370 0.712
Gendermale 27.93648 57.58191 0.485 0.628
ORdate_epoch 0.10075 0.07174 1.404 0.162
Hypertension.compositeyes 41.59254 75.70297 0.549 0.583
DiabetesStatusDiabetes -79.12648 62.34892 -1.269 0.206
SmokerStatusEx-smoker -24.79177 55.80427 -0.444 0.657
SmokerStatusNever smoked -86.25570 72.62906 -1.188 0.237
Med.Statin.LLDyes -62.25468 55.93172 -1.113 0.267
Med.all.antiplateletyes -83.62314 87.52348 -0.955 0.341
GFR_MDRD -1.76925 1.35443 -1.306 0.193
BMI -1.34071 6.24865 -0.215 0.830
MedHx_CVDNo 11.86909 51.08519 0.232 0.817
stenose50-70% 68.43377 395.20937 0.173 0.863
stenose70-90% 85.04846 343.23139 0.248 0.805
stenose90-99% 77.08834 342.52342 0.225 0.822
stenose100% (Occlusion) -5.89327 429.81606 -0.014 0.989
Residual standard error: 331.6 on 184 degrees of freedom
Multiple R-squared: 0.04903, Adjusted R-squared: -0.03883
F-statistic: 0.5581 on 17 and 184 DF, p-value: 0.9187
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MIF_rank
Effect size...............: 42.58446
Standard error............: 28.70174
Odds ratio (effect size)..: 3.120309e+18
Lower 95% CI..............: 0
Upper 95% CI..............: 8.425809e+42
T-value...................: 1.483689
P-value...................: 0.1396023
R^2.......................: 0.049033
Adjusted r^2..............: -0.038828
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MCP1_rankfilter: removed 422 rows (68%), 200 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
243.1
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-381.53 -140.95 -54.62 38.91 3090.37
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -500.71499 998.17747 -0.502 0.617
currentDF[, TRAIT] 35.91094 24.57144 1.461 0.146
Age -0.64547 3.43022 -0.188 0.851
Gendermale 21.68959 58.49053 0.371 0.711
ORdate_epoch 0.07555 0.06606 1.144 0.254
Hypertension.compositeyes 39.21185 76.06512 0.516 0.607
DiabetesStatusDiabetes -80.54276 62.74837 -1.284 0.201
SmokerStatusEx-smoker -33.92924 56.00120 -0.606 0.545
SmokerStatusNever smoked -92.78949 73.34844 -1.265 0.207
Med.Statin.LLDyes -57.09875 56.37604 -1.013 0.312
Med.all.antiplateletyes -110.19940 92.87553 -1.187 0.237
GFR_MDRD -2.15154 1.34509 -1.600 0.111
BMI 0.19588 6.36651 0.031 0.975
MedHx_CVDNo 7.33760 51.50032 0.142 0.887
stenose50-70% 107.50288 395.33754 0.272 0.786
stenose70-90% 117.22661 342.97396 0.342 0.733
stenose90-99% 122.27141 341.77476 0.358 0.721
stenose100% (Occlusion) 24.83669 431.84308 0.058 0.954
Residual standard error: 332.5 on 182 degrees of freedom
Multiple R-squared: 0.05254, Adjusted R-squared: -0.03595
F-statistic: 0.5937 on 17 and 182 DF, p-value: 0.894
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MCP1_rank
Effect size...............: 35.91094
Standard error............: 24.57144
Odds ratio (effect size)..: 3.943889e+15
Lower 95% CI..............: 0
Upper 95% CI..............: 3.247574e+36
T-value...................: 1.461491
P-value...................: 0.1456051
R^2.......................: 0.052544
Adjusted r^2..............: -0.035955
Sample size of AE DB......: 622
Sample size of model......: 200
Missing data %............: 67.84566
- processing MIP1a_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
235.78 41.82
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-341.81 -132.95 -52.18 47.79 3091.88
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -50.02159 949.24544 -0.053 0.958
currentDF[, TRAIT] 38.12778 24.37065 1.564 0.120
Age -2.72637 3.37808 -0.807 0.421
Gendermale 50.40994 58.36560 0.864 0.389
ORdate_epoch 0.04184 0.06264 0.668 0.505
Hypertension.compositeyes 84.74853 76.45651 1.108 0.269
DiabetesStatusDiabetes -73.66856 63.03109 -1.169 0.244
SmokerStatusEx-smoker -17.17287 56.48504 -0.304 0.761
SmokerStatusNever smoked -69.97761 72.57658 -0.964 0.336
Med.Statin.LLDyes -46.25944 56.38082 -0.820 0.413
Med.all.antiplateletyes 1.12804 94.64381 0.012 0.991
GFR_MDRD -1.54156 1.31782 -1.170 0.244
BMI -2.56959 6.24055 -0.412 0.681
MedHx_CVDNo -11.37609 52.33424 -0.217 0.828
stenose50-70% 32.01360 410.43805 0.078 0.938
stenose70-90% 77.67143 332.35743 0.234 0.815
stenose90-99% 79.13719 331.81675 0.238 0.812
stenose100% (Occlusion) 61.23816 418.48362 0.146 0.884
Residual standard error: 320.4 on 171 degrees of freedom
Multiple R-squared: 0.05298, Adjusted R-squared: -0.04117
F-statistic: 0.5628 on 17 and 171 DF, p-value: 0.9153
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MIP1a_rank
Effect size...............: 38.12778
Standard error............: 24.37065
Odds ratio (effect size)..: 3.619789e+16
Lower 95% CI..............: 0
Upper 95% CI..............: 2.010937e+37
T-value...................: 1.564496
P-value...................: 0.1195499
R^2.......................: 0.052982
Adjusted r^2..............: -0.041166
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing RANTES_rankfilter: removed 424 rows (68%), 198 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
242.5
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-350.40 -148.30 -66.91 36.80 3121.71
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -398.37766 1048.07446 -0.380 0.704
currentDF[, TRAIT] 20.40994 27.49949 0.742 0.459
Age -1.14433 3.50000 -0.327 0.744
Gendermale 26.16119 59.88022 0.437 0.663
ORdate_epoch 0.07151 0.07066 1.012 0.313
Hypertension.compositeyes 34.60088 79.41462 0.436 0.664
DiabetesStatusDiabetes -75.79693 64.74275 -1.171 0.243
SmokerStatusEx-smoker -27.09380 57.76591 -0.469 0.640
SmokerStatusNever smoked -81.51674 74.70162 -1.091 0.277
Med.Statin.LLDyes -57.82350 57.31030 -1.009 0.314
Med.all.antiplateletyes -80.35812 93.48740 -0.860 0.391
GFR_MDRD -2.02034 1.36618 -1.479 0.141
BMI -1.66582 6.45305 -0.258 0.797
MedHx_CVDNo 14.33648 53.58532 0.268 0.789
stenose50-70% 106.76195 427.57292 0.250 0.803
stenose70-90% 105.57491 348.81314 0.303 0.762
stenose90-99% 107.92478 347.05109 0.311 0.756
stenose100% (Occlusion) -10.79076 439.90155 -0.025 0.980
Residual standard error: 336.7 on 180 degrees of freedom
Multiple R-squared: 0.04011, Adjusted R-squared: -0.05055
F-statistic: 0.4424 on 17 and 180 DF, p-value: 0.9731
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: RANTES_rank
Effect size...............: 20.40994
Standard error............: 27.49949
Odds ratio (effect size)..: 731011166
Lower 95% CI..............: 0
Upper 95% CI..............: 1.870506e+32
T-value...................: 0.742193
P-value...................: 0.4589375
R^2.......................: 0.040108
Adjusted r^2..............: -0.050548
Sample size of AE DB......: 622
Sample size of model......: 198
Missing data %............: 68.1672
- processing MIG_rankfilter: removed 423 rows (68%), 199 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] GFR_MDRD
373.06 43.62 -1.92
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-342.66 -144.88 -51.64 45.32 3068.02
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 341.37410 1006.47413 0.339 0.7349
currentDF[, TRAIT] 52.50181 27.07193 1.939 0.0540 .
Age -0.47832 3.45081 -0.139 0.8899
Gendermale 17.11142 58.56656 0.292 0.7705
ORdate_epoch 0.01251 0.06769 0.185 0.8536
Hypertension.compositeyes 33.40183 77.64654 0.430 0.6676
DiabetesStatusDiabetes -83.47022 63.51663 -1.314 0.1905
SmokerStatusEx-smoker -48.98919 56.81833 -0.862 0.3897
SmokerStatusNever smoked -102.86265 74.34930 -1.384 0.1682
Med.Statin.LLDyes -61.75967 56.82714 -1.087 0.2786
Med.all.antiplateletyes -80.33915 89.32081 -0.899 0.3696
GFR_MDRD -2.31071 1.34201 -1.722 0.0868 .
BMI -0.92000 6.26735 -0.147 0.8835
MedHx_CVDNo 8.05694 52.01816 0.155 0.8771
stenose50-70% 47.09097 424.25622 0.111 0.9117
stenose70-90% 97.88110 343.14204 0.285 0.7758
stenose90-99% 79.37698 342.35728 0.232 0.8169
stenose100% (Occlusion) 8.52115 431.12799 0.020 0.9843
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 331.9 on 181 degrees of freedom
Multiple R-squared: 0.05856, Adjusted R-squared: -0.02987
F-statistic: 0.6622 on 17 and 181 DF, p-value: 0.8368
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MIG_rank
Effect size...............: 52.50181
Standard error............: 27.07193
Odds ratio (effect size)..: 6.327687e+22
Lower 95% CI..............: 0.572
Upper 95% CI..............: 7.003908e+45
T-value...................: 1.939345
P-value...................: 0.05401412
R^2.......................: 0.058556
Adjusted r^2..............: -0.029867
Sample size of AE DB......: 622
Sample size of model......: 199
Missing data %............: 68.00643
- processing IP10_rankfilter: removed 439 rows (71%), 183 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
233.87 40.69
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-337.03 -135.20 -55.33 53.99 3058.38
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -236.03124 969.55781 -0.243 0.8080
currentDF[, TRAIT] 48.28820 25.95751 1.860 0.0646 .
Age -2.55084 3.54275 -0.720 0.4725
Gendermale 64.11111 58.15825 1.102 0.2719
ORdate_epoch 0.05645 0.06378 0.885 0.3774
Hypertension.compositeyes 88.21497 78.73028 1.120 0.2641
DiabetesStatusDiabetes -78.88847 65.96501 -1.196 0.2334
SmokerStatusEx-smoker -41.83193 58.69960 -0.713 0.4771
SmokerStatusNever smoked -79.09848 75.60730 -1.046 0.2970
Med.Statin.LLDyes -50.40762 58.20576 -0.866 0.3877
Med.all.antiplateletyes 8.50161 90.14359 0.094 0.9250
GFR_MDRD -1.89645 1.38404 -1.370 0.1725
BMI -2.93184 6.26835 -0.468 0.6406
MedHx_CVDNo -31.99951 53.27840 -0.601 0.5489
stenose50-70% 52.94048 414.52316 0.128 0.8985
stenose70-90% 99.43545 336.35188 0.296 0.7679
stenose90-99% 108.87242 334.44273 0.326 0.7452
stenose100% (Occlusion) 147.35399 422.48970 0.349 0.7277
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 324.1 on 165 degrees of freedom
Multiple R-squared: 0.06349, Adjusted R-squared: -0.033
F-statistic: 0.658 on 17 and 165 DF, p-value: 0.8402
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: IP10_rank
Effect size...............: 48.2882
Standard error............: 25.95751
Odds ratio (effect size)..: 9.360536e+20
Lower 95% CI..............: 0.075
Upper 95% CI..............: 1.166211e+43
T-value...................: 1.860279
P-value...................: 0.06462592
R^2.......................: 0.063486
Adjusted r^2..............: -0.033003
Sample size of AE DB......: 622
Sample size of model......: 183
Missing data %............: 70.57878
- processing Eotaxin1_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
241.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-352.85 -143.19 -50.72 34.02 3098.84
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 41.38963 981.54113 0.042 0.966
currentDF[, TRAIT] 31.20422 25.17114 1.240 0.217
Age -1.08891 3.40974 -0.319 0.750
Gendermale 23.08163 58.07732 0.397 0.692
ORdate_epoch 0.03701 0.06523 0.567 0.571
Hypertension.compositeyes 28.17463 75.32464 0.374 0.709
DiabetesStatusDiabetes -79.36488 62.50306 -1.270 0.206
SmokerStatusEx-smoker -37.84302 56.05104 -0.675 0.500
SmokerStatusNever smoked -91.42957 73.51167 -1.244 0.215
Med.Statin.LLDyes -62.39457 56.06652 -1.113 0.267
Med.all.antiplateletyes -79.01301 87.51684 -0.903 0.368
GFR_MDRD -2.07213 1.33757 -1.549 0.123
BMI -0.76996 6.26185 -0.123 0.902
MedHx_CVDNo 11.12579 51.17734 0.217 0.828
stenose50-70% 77.91366 395.77120 0.197 0.844
stenose70-90% 101.14750 343.29696 0.295 0.769
stenose90-99% 88.13261 342.93390 0.257 0.797
stenose100% (Occlusion) 0.16109 430.54559 0.000 1.000
Residual standard error: 332.2 on 184 degrees of freedom
Multiple R-squared: 0.04563, Adjusted R-squared: -0.04255
F-statistic: 0.5175 on 17 and 184 DF, p-value: 0.9422
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 31.20422
Standard error............: 25.17114
Odds ratio (effect size)..: 3.563025e+13
Lower 95% CI..............: 0
Upper 95% CI..............: 9.50437e+34
T-value...................: 1.239682
P-value...................: 0.2166716
R^2.......................: 0.045627
Adjusted r^2..............: -0.042549
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing TARC_rankfilter: removed 444 rows (71%), 178 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
248.3
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-335.69 -152.80 -58.95 39.72 3073.30
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -672.37946 1228.63525 -0.547 0.585
currentDF[, TRAIT] 44.81707 29.42016 1.523 0.130
Age 0.14609 3.89090 0.038 0.970
Gendermale 44.50452 65.58759 0.679 0.498
ORdate_epoch 0.08434 0.08523 0.990 0.324
Hypertension.compositeyes 43.81156 82.67150 0.530 0.597
DiabetesStatusDiabetes -86.91134 69.20873 -1.256 0.211
SmokerStatusEx-smoker -36.54594 62.78941 -0.582 0.561
SmokerStatusNever smoked -99.98010 80.22880 -1.246 0.215
Med.Statin.LLDyes -86.65601 65.29743 -1.327 0.186
Med.all.antiplateletyes -76.72540 101.19983 -0.758 0.449
GFR_MDRD -1.90164 1.51536 -1.255 0.211
BMI -1.45280 6.99756 -0.208 0.836
MedHx_CVDNo 9.75870 57.62889 0.169 0.866
stenose50-70% 94.96007 442.48460 0.215 0.830
stenose70-90% 148.70151 361.54597 0.411 0.681
stenose90-99% 113.85259 360.33641 0.316 0.752
stenose100% (Occlusion) 53.35200 458.85084 0.116 0.908
Residual standard error: 349.5 on 160 degrees of freedom
Multiple R-squared: 0.05629, Adjusted R-squared: -0.04398
F-statistic: 0.5614 on 17 and 160 DF, p-value: 0.9158
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: TARC_rank
Effect size...............: 44.81707
Standard error............: 29.42016
Odds ratio (effect size)..: 2.909413e+19
Lower 95% CI..............: 0
Upper 95% CI..............: 3.211846e+44
T-value...................: 1.523345
P-value...................: 0.1296464
R^2.......................: 0.056287
Adjusted r^2..............: -0.043983
Sample size of AE DB......: 622
Sample size of model......: 178
Missing data %............: 71.38264
- processing PARC_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
241.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-338.31 -147.06 -62.63 37.37 3061.38
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -633.23658 1047.38862 -0.605 0.546
currentDF[, TRAIT] 31.92799 26.64862 1.198 0.232
Age -1.30160 3.39230 -0.384 0.702
Gendermale 34.42606 57.66172 0.597 0.551
ORdate_epoch 0.08478 0.06951 1.220 0.224
Hypertension.compositeyes 42.66098 76.26176 0.559 0.577
DiabetesStatusDiabetes -80.44236 62.46900 -1.288 0.199
SmokerStatusEx-smoker -33.42161 55.81839 -0.599 0.550
SmokerStatusNever smoked -89.38284 73.32308 -1.219 0.224
Med.Statin.LLDyes -61.24124 56.03752 -1.093 0.276
Med.all.antiplateletyes -66.27085 87.54919 -0.757 0.450
GFR_MDRD -1.90200 1.34879 -1.410 0.160
BMI -0.28922 6.29272 -0.046 0.963
MedHx_CVDNo 5.03829 51.12627 0.099 0.922
stenose50-70% 110.40154 394.99516 0.280 0.780
stenose70-90% 121.39252 342.70310 0.354 0.724
stenose90-99% 125.83260 341.41359 0.369 0.713
stenose100% (Occlusion) 64.94744 431.49605 0.151 0.881
Residual standard error: 332.3 on 184 degrees of freedom
Multiple R-squared: 0.04511, Adjusted R-squared: -0.04312
F-statistic: 0.5113 on 17 and 184 DF, p-value: 0.9453
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: PARC_rank
Effect size...............: 31.92799
Standard error............: 26.64862
Odds ratio (effect size)..: 7.347673e+13
Lower 95% CI..............: 0
Upper 95% CI..............: 3.547398e+36
T-value...................: 1.198111
P-value...................: 0.2324149
R^2.......................: 0.045105
Adjusted r^2..............: -0.043119
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MDC_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
235.74 41.62
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-324.85 -139.76 -48.50 49.19 3053.28
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -632.42256 974.06990 -0.649 0.5170
currentDF[, TRAIT] 49.10940 25.60441 1.918 0.0568 .
Age -2.55300 3.35583 -0.761 0.4478
Gendermale 57.17186 57.77451 0.990 0.3238
ORdate_epoch 0.08383 0.06503 1.289 0.1991
Hypertension.compositeyes 88.63692 76.13924 1.164 0.2460
DiabetesStatusDiabetes -65.08819 63.33261 -1.028 0.3055
SmokerStatusEx-smoker -11.47604 55.99669 -0.205 0.8379
SmokerStatusNever smoked -72.44354 72.09595 -1.005 0.3164
Med.Statin.LLDyes -57.85666 56.81205 -1.018 0.3099
Med.all.antiplateletyes 17.02501 94.53368 0.180 0.8573
GFR_MDRD -1.41316 1.30781 -1.081 0.2814
BMI -2.30237 6.24818 -0.368 0.7130
MedHx_CVDNo -10.74087 52.13389 -0.206 0.8370
stenose50-70% 10.53659 409.20891 0.026 0.9795
stenose70-90% 83.65712 330.59586 0.253 0.8005
stenose90-99% 88.94072 329.47058 0.270 0.7875
stenose100% (Occlusion) 82.31782 416.64127 0.198 0.8436
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 319.3 on 171 degrees of freedom
Multiple R-squared: 0.05936, Adjusted R-squared: -0.03415
F-statistic: 0.6348 on 17 and 171 DF, p-value: 0.8608
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MDC_rank
Effect size...............: 49.1094
Standard error............: 25.60441
Odds ratio (effect size)..: 2.127856e+21
Lower 95% CI..............: 0.341
Upper 95% CI..............: 1.326962e+43
T-value...................: 1.918005
P-value...................: 0.05677668
R^2.......................: 0.059363
Adjusted r^2..............: -0.034151
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing OPG_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
241.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-346.34 -149.14 -63.96 32.10 3122.04
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -144.48733 974.35872 -0.148 0.882
currentDF[, TRAIT] -4.69377 24.36674 -0.193 0.847
Age -1.78057 3.44061 -0.518 0.605
Gendermale 32.84300 57.96043 0.567 0.572
ORdate_epoch 0.05217 0.06431 0.811 0.418
Hypertension.compositeyes 27.28580 75.90721 0.359 0.720
DiabetesStatusDiabetes -85.50706 62.60091 -1.366 0.174
SmokerStatusEx-smoker -29.22470 56.54791 -0.517 0.606
SmokerStatusNever smoked -73.77177 73.39702 -1.005 0.316
Med.Statin.LLDyes -57.69002 56.26460 -1.025 0.307
Med.all.antiplateletyes -71.04248 88.10161 -0.806 0.421
GFR_MDRD -2.10992 1.34266 -1.571 0.118
BMI -1.17641 6.30674 -0.187 0.852
MedHx_CVDNo 7.27109 51.28855 0.142 0.887
stenose50-70% 112.61597 396.61922 0.284 0.777
stenose70-90% 128.40141 343.96910 0.373 0.709
stenose90-99% 129.46053 342.69451 0.378 0.706
stenose100% (Occlusion) 18.34790 433.17245 0.042 0.966
Residual standard error: 333.6 on 184 degrees of freedom
Multiple R-squared: 0.03785, Adjusted R-squared: -0.05104
F-statistic: 0.4258 on 17 and 184 DF, p-value: 0.978
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: OPG_rank
Effect size...............: -4.693769
Standard error............: 24.36674
Odds ratio (effect size)..: 0.009
Lower 95% CI..............: 0
Upper 95% CI..............: 5.045526e+18
T-value...................: -0.19263
P-value...................: 0.8474611
R^2.......................: 0.03785
Adjusted r^2..............: -0.051045
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing sICAM1_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
241.9
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-354.44 -145.83 -57.59 35.14 3090.30
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -385.45750 996.02109 -0.387 0.699
currentDF[, TRAIT] 25.81962 25.54712 1.011 0.314
Age -0.97238 3.45167 -0.282 0.778
Gendermale 30.09948 57.73059 0.521 0.603
ORdate_epoch 0.06892 0.06617 1.042 0.299
Hypertension.compositeyes 36.57434 75.84734 0.482 0.630
DiabetesStatusDiabetes -80.43930 62.58970 -1.285 0.200
SmokerStatusEx-smoker -33.02493 55.88151 -0.591 0.555
SmokerStatusNever smoked -88.67676 73.63686 -1.204 0.230
Med.Statin.LLDyes -59.32121 56.05421 -1.058 0.291
Med.all.antiplateletyes -75.75228 87.54124 -0.865 0.388
GFR_MDRD -1.97792 1.34543 -1.470 0.143
BMI -1.25520 6.26854 -0.200 0.842
MedHx_CVDNo 13.11488 51.48313 0.255 0.799
stenose50-70% 92.69093 395.83752 0.234 0.815
stenose70-90% 96.26706 344.57435 0.279 0.780
stenose90-99% 98.11041 343.18979 0.286 0.775
stenose100% (Occlusion) 0.04103 431.38339 0.000 1.000
Residual standard error: 332.7 on 184 degrees of freedom
Multiple R-squared: 0.04297, Adjusted R-squared: -0.04545
F-statistic: 0.4859 on 17 and 184 DF, p-value: 0.9571
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: sICAM1_rank
Effect size...............: 25.81962
Standard error............: 25.54712
Odds ratio (effect size)..: 163424446378
Lower 95% CI..............: 0
Upper 95% CI..............: 9.10881e+32
T-value...................: 1.010667
P-value...................: 0.3135032
R^2.......................: 0.042968
Adjusted r^2..............: -0.045453
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing VEGFA_rankfilter: removed 445 rows (72%), 177 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
246.8
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-338.68 -151.21 -70.42 37.67 3124.10
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -227.62798 1106.14409 -0.206 0.837
currentDF[, TRAIT] -1.00134 29.61715 -0.034 0.973
Age -0.49405 3.87624 -0.127 0.899
Gendermale 35.66493 65.72023 0.543 0.588
ORdate_epoch 0.06318 0.08222 0.768 0.443
Hypertension.compositeyes 30.34132 87.80973 0.346 0.730
DiabetesStatusDiabetes -93.46439 70.50848 -1.326 0.187
SmokerStatusEx-smoker -21.78413 62.68701 -0.348 0.729
SmokerStatusNever smoked -59.06716 85.85415 -0.688 0.492
Med.Statin.LLDyes -58.67762 63.01597 -0.931 0.353
Med.all.antiplateletyes -65.23866 94.12293 -0.693 0.489
GFR_MDRD -2.03889 1.39327 -1.463 0.145
BMI -1.63872 7.20873 -0.227 0.820
MedHx_CVDNo 11.43260 58.52723 0.195 0.845
stenose70-90% -5.77619 269.98559 -0.021 0.983
stenose90-99% -22.92581 267.46881 -0.086 0.932
stenose100% (Occlusion) -110.20429 380.77216 -0.289 0.773
Residual standard error: 352 on 160 degrees of freedom
Multiple R-squared: 0.03699, Adjusted R-squared: -0.05931
F-statistic: 0.3841 on 16 and 160 DF, p-value: 0.9846
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: VEGFA_rank
Effect size...............: -1.001337
Standard error............: 29.61715
Odds ratio (effect size)..: 0.367
Lower 95% CI..............: 0
Upper 95% CI..............: 5.966993e+24
T-value...................: -0.033809
P-value...................: 0.9730713
R^2.......................: 0.036992
Adjusted r^2..............: -0.059309
Sample size of AE DB......: 622
Sample size of model......: 177
Missing data %............: 71.54341
- processing TGFB_rankfilter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes GFR_MDRD
386.186 -83.192 -1.748
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-349.98 -151.60 -57.84 43.76 3117.85
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -147.68278 963.52354 -0.153 0.878
currentDF[, TRAIT] 6.87291 24.45742 0.281 0.779
Age -1.73273 3.41640 -0.507 0.613
Gendermale 24.81947 57.32164 0.433 0.666
ORdate_epoch 0.05489 0.06399 0.858 0.392
Hypertension.compositeyes 38.53812 74.52005 0.517 0.606
DiabetesStatusDiabetes -91.74773 60.76243 -1.510 0.133
SmokerStatusEx-smoker -21.72899 55.81681 -0.389 0.698
SmokerStatusNever smoked -75.19337 74.03820 -1.016 0.311
Med.Statin.LLDyes -51.97702 56.00993 -0.928 0.355
Med.all.antiplateletyes -86.32257 90.02816 -0.959 0.339
GFR_MDRD -2.15082 1.33576 -1.610 0.109
BMI -1.82763 6.32021 -0.289 0.773
MedHx_CVDNo 8.26504 51.12194 0.162 0.872
stenose50-70% 105.54088 397.98398 0.265 0.791
stenose70-90% 125.27842 343.73077 0.364 0.716
stenose90-99% 123.03531 342.57136 0.359 0.720
stenose100% (Occlusion) -6.10423 434.55960 -0.014 0.989
Residual standard error: 332.6 on 185 degrees of freedom
Multiple R-squared: 0.04018, Adjusted R-squared: -0.04802
F-statistic: 0.4555 on 17 and 185 DF, p-value: 0.9688
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: TGFB_rank
Effect size...............: 6.872906
Standard error............: 24.45742
Odds ratio (effect size)..: 965.751
Lower 95% CI..............: 0
Upper 95% CI..............: 6.359778e+23
T-value...................: 0.281015
P-value...................: 0.7790128
R^2.......................: 0.040178
Adjusted r^2..............: -0.048022
Sample size of AE DB......: 622
Sample size of model......: 203
Missing data %............: 67.36334
- processing MMP2_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
360.725 -1.721
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-318.42 -147.02 -56.17 47.40 3111.58
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 257.43781 990.00895 0.260 0.7951
currentDF[, TRAIT] -25.01480 25.27122 -0.990 0.3235
Age -2.41784 3.36999 -0.717 0.4740
Gendermale 8.64954 57.00626 0.152 0.8796
ORdate_epoch 0.02981 0.06535 0.456 0.6489
Hypertension.compositeyes 15.20505 77.07375 0.197 0.8438
DiabetesStatusDiabetes -88.25297 61.72145 -1.430 0.1545
SmokerStatusEx-smoker -13.08655 55.83633 -0.234 0.8150
SmokerStatusNever smoked -57.34041 73.72530 -0.778 0.4377
Med.Statin.LLDyes -62.45485 55.45371 -1.126 0.2615
Med.all.antiplateletyes -69.22437 87.06919 -0.795 0.4276
GFR_MDRD -2.40356 1.29184 -1.861 0.0644 .
BMI -2.19867 6.42424 -0.342 0.7326
MedHx_CVDNo 4.94581 51.14162 0.097 0.9231
stenose50-70% 121.03737 394.20060 0.307 0.7592
stenose70-90% 122.04580 342.20859 0.357 0.7218
stenose90-99% 117.81449 340.73689 0.346 0.7299
stenose100% (Occlusion) -0.76762 429.87811 -0.002 0.9986
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 331.3 on 184 degrees of freedom
Multiple R-squared: 0.04399, Adjusted R-squared: -0.04434
F-statistic: 0.498 on 17 and 184 DF, p-value: 0.9517
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MMP2_rank
Effect size...............: -25.0148
Standard error............: 25.27123
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 44413097706
T-value...................: -0.989853
P-value...................: 0.3235458
R^2.......................: 0.043988
Adjusted r^2..............: -0.044339
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MMP8_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
240.08 47.11
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-411.85 -134.97 -55.57 51.85 3114.37
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 216.92798 968.82891 0.224 0.8231
currentDF[, TRAIT] 42.83720 24.27166 1.765 0.0792 .
Age -1.69719 3.34072 -0.508 0.6120
Gendermale -1.51679 57.07025 -0.027 0.9788
ORdate_epoch 0.03691 0.06395 0.577 0.5646
Hypertension.compositeyes 27.03879 75.65207 0.357 0.7212
DiabetesStatusDiabetes -75.77466 61.83690 -1.225 0.2220
SmokerStatusEx-smoker -11.67688 55.44710 -0.211 0.8334
SmokerStatusNever smoked -66.31237 72.64636 -0.913 0.3625
Med.Statin.LLDyes -61.29266 55.13420 -1.112 0.2677
Med.all.antiplateletyes -67.15497 86.49929 -0.776 0.4385
GFR_MDRD -1.88586 1.28908 -1.463 0.1452
BMI -2.68692 6.39352 -0.420 0.6748
MedHx_CVDNo 15.30870 51.08063 0.300 0.7647
stenose50-70% 10.18413 394.76483 0.026 0.9794
stenose70-90% -19.64721 348.13793 -0.056 0.9551
stenose90-99% -8.39482 345.31447 -0.024 0.9806
stenose100% (Occlusion) -142.91578 435.06622 -0.328 0.7429
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 329.4 on 184 degrees of freedom
Multiple R-squared: 0.0549, Adjusted R-squared: -0.03242
F-statistic: 0.6287 on 17 and 184 DF, p-value: 0.8664
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MMP8_rank
Effect size...............: 42.8372
Standard error............: 24.27166
Odds ratio (effect size)..: 4.017524e+18
Lower 95% CI..............: 0.009
Upper 95% CI..............: 1.838277e+39
T-value...................: 1.764906
P-value...................: 0.07923853
R^2.......................: 0.054897
Adjusted r^2..............: -0.032423
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MMP9_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
239.91 51.04
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-407.14 -139.32 -59.91 51.47 3085.93
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -191.03538 968.13355 -0.197 0.8438
currentDF[, TRAIT] 51.62078 23.87100 2.162 0.0319 *
Age -1.34039 3.33743 -0.402 0.6884
Gendermale 7.78308 56.10389 0.139 0.8898
ORdate_epoch 0.05838 0.06411 0.911 0.3637
Hypertension.compositeyes 55.43247 76.44738 0.725 0.4693
DiabetesStatusDiabetes -72.64380 61.58908 -1.179 0.2397
SmokerStatusEx-smoker -20.44436 55.14532 -0.371 0.7113
SmokerStatusNever smoked -78.56971 72.53794 -1.083 0.2802
Med.Statin.LLDyes -66.19372 54.94111 -1.205 0.2298
Med.all.antiplateletyes -86.73659 86.21115 -1.006 0.3157
GFR_MDRD -1.74384 1.28869 -1.353 0.1777
BMI -2.37232 6.35142 -0.374 0.7092
MedHx_CVDNo 15.79691 50.80552 0.311 0.7562
stenose50-70% 84.49535 389.86468 0.217 0.8287
stenose70-90% 76.28124 339.07694 0.225 0.8223
stenose90-99% 80.07091 337.58799 0.237 0.8128
stenose100% (Occlusion) -10.88774 425.67670 -0.026 0.9796
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 328 on 184 degrees of freedom
Multiple R-squared: 0.06272, Adjusted R-squared: -0.02388
F-statistic: 0.7243 on 17 and 184 DF, p-value: 0.7755
Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: LDLR
Trait/outcome.............: MMP9_rank
Effect size...............: 51.62078
Standard error............: 23.871
Odds ratio (effect size)..: 2.62193e+22
Lower 95% CI..............: 125.665
Upper 95% CI..............: 5.470522e+42
T-value...................: 2.162489
P-value...................: 0.0318711
R^2.......................: 0.062718
Adjusted r^2..............: -0.023878
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
Analysis of CD36.
- processing IL2_rankfilter: removed 459 rows (74%), 163 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + MedHx_CVD,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch MedHx_CVDNo
-949.75469 0.08933 -50.15658
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-208.5 -116.7 -55.9 37.0 866.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -833.56645 639.51509 -1.303 0.1945
currentDF[, TRAIT] -11.88969 16.52742 -0.719 0.4730
Age -0.84475 2.20758 -0.383 0.7025
Gendermale -7.79328 39.71358 -0.196 0.8447
ORdate_epoch 0.09291 0.04561 2.037 0.0435 *
Hypertension.compositeyes 12.33547 51.70666 0.239 0.8118
DiabetesStatusDiabetes -10.01436 43.32640 -0.231 0.8175
SmokerStatusEx-smoker 7.64400 37.13123 0.206 0.8372
SmokerStatusNever smoked -37.80549 50.55629 -0.748 0.4558
Med.Statin.LLDyes -27.08885 36.47605 -0.743 0.4589
Med.all.antiplateletyes -28.59872 57.80041 -0.495 0.6215
GFR_MDRD -0.53303 0.94856 -0.562 0.5750
BMI -3.54894 4.92142 -0.721 0.4720
MedHx_CVDNo -47.06710 34.50970 -1.364 0.1747
stenose70-90% 68.03468 147.65630 0.461 0.6457
stenose90-99% 75.93403 146.71074 0.518 0.6055
stenose100% (Occlusion) -27.13955 213.68709 -0.127 0.8991
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 196.9 on 146 degrees of freedom
Multiple R-squared: 0.05769, Adjusted R-squared: -0.04557
F-statistic: 0.5587 on 16 and 146 DF, p-value: 0.9098
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL2_rank
Effect size...............: -11.88969
Standard error............: 16.52742
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 803152200
T-value...................: -0.719392
P-value...................: 0.4730496
R^2.......................: 0.057692
Adjusted r^2..............: -0.045574
Sample size of AE DB......: 622
Sample size of model......: 163
Missing data %............: 73.79421
- processing IL4_rankfilter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + MedHx_CVD,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch MedHx_CVDNo
-1201.4130 0.1102 -56.7764
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-231.75 -127.26 -55.59 34.59 860.24
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.142e+03 7.342e+02 -1.555 0.1224
currentDF[, TRAIT] -1.978e+00 1.896e+01 -0.104 0.9171
Age -5.899e-01 2.484e+00 -0.237 0.8127
Gendermale -1.580e+00 4.438e+01 -0.036 0.9716
ORdate_epoch 1.172e-01 5.267e-02 2.224 0.0279 *
Hypertension.compositeyes 1.515e+01 5.829e+01 0.260 0.7953
DiabetesStatusDiabetes 2.295e+00 4.849e+01 0.047 0.9623
SmokerStatusEx-smoker 3.434e+00 4.045e+01 0.085 0.9325
SmokerStatusNever smoked -3.418e+01 5.748e+01 -0.595 0.5531
Med.Statin.LLDyes -3.468e+01 4.243e+01 -0.817 0.4152
Med.all.antiplateletyes -1.457e+01 6.368e+01 -0.229 0.8194
GFR_MDRD -7.768e-01 1.157e+00 -0.671 0.5031
BMI -3.908e+00 5.794e+00 -0.674 0.5012
MedHx_CVDNo -5.293e+01 3.860e+01 -1.371 0.1727
stenose70-90% 9.045e+01 1.570e+02 0.576 0.5656
stenose90-99% 7.807e+01 1.558e+02 0.501 0.6171
stenose100% (Occlusion) -4.452e+00 2.276e+02 -0.020 0.9844
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 206.9 on 128 degrees of freedom
Multiple R-squared: 0.06752, Adjusted R-squared: -0.04904
F-statistic: 0.5793 on 16 and 128 DF, p-value: 0.8947
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL4_rank
Effect size...............: -1.977614
Standard error............: 18.95543
Odds ratio (effect size)..: 0.138
Lower 95% CI..............: 0
Upper 95% CI..............: 1.88937e+15
T-value...................: -0.10433
P-value...................: 0.917071
R^2.......................: 0.067521
Adjusted r^2..............: -0.049038
Sample size of AE DB......: 622
Sample size of model......: 145
Missing data %............: 76.6881
- processing IL5_rankfilter: removed 464 rows (75%), 158 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + MedHx_CVD,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch MedHx_CVDNo
-996.25226 0.09303 -51.18540
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-203.63 -114.26 -57.87 35.17 879.25
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -829.23484 666.70865 -1.244 0.2156
currentDF[, TRAIT] -11.56428 16.99859 -0.680 0.4974
Age -0.58578 2.35273 -0.249 0.8037
Gendermale 0.31920 41.03179 0.008 0.9938
ORdate_epoch 0.09026 0.04864 1.856 0.0656 .
Hypertension.compositeyes 7.85714 54.37057 0.145 0.8853
DiabetesStatusDiabetes -6.47053 44.19658 -0.146 0.8838
SmokerStatusEx-smoker 0.05622 37.37777 0.002 0.9988
SmokerStatusNever smoked -31.26109 53.98021 -0.579 0.5634
Med.Statin.LLDyes -21.50863 38.45416 -0.559 0.5768
Med.all.antiplateletyes -26.59957 59.56613 -0.447 0.6559
GFR_MDRD -0.45929 1.02146 -0.450 0.6537
BMI -2.08990 5.07086 -0.412 0.6809
MedHx_CVDNo -47.58267 36.27158 -1.312 0.1917
stenose70-90% 35.32848 125.54672 0.281 0.7788
stenose90-99% 32.03121 124.01379 0.258 0.7966
stenose100% (Occlusion) -61.36701 200.42559 -0.306 0.7599
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 200.8 on 141 degrees of freedom
Multiple R-squared: 0.0497, Adjusted R-squared: -0.05813
F-statistic: 0.4609 on 16 and 141 DF, p-value: 0.9614
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL5_rank
Effect size...............: -11.56428
Standard error............: 16.99859
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 2800191696
T-value...................: -0.680308
P-value...................: 0.4974249
R^2.......................: 0.049701
Adjusted r^2..............: -0.058135
Sample size of AE DB......: 622
Sample size of model......: 158
Missing data %............: 74.59807
- processing IL6_rankfilter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + MedHx_CVD,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch MedHx_CVDNo
-890.1511 0.0836 -43.0696
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-210.12 -105.36 -46.86 25.77 882.81
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.042e+03 6.374e+02 -1.636 0.1041
currentDF[, TRAIT] -1.205e+01 1.528e+01 -0.789 0.4313
Age -1.051e+00 2.233e+00 -0.471 0.6386
Gendermale -1.651e+01 3.675e+01 -0.449 0.6539
ORdate_epoch 9.902e-02 4.247e-02 2.332 0.0211 *
Hypertension.compositeyes 6.425e+00 4.910e+01 0.131 0.8961
DiabetesStatusDiabetes 2.700e+00 4.108e+01 0.066 0.9477
SmokerStatusEx-smoker 4.204e+00 3.524e+01 0.119 0.9052
SmokerStatusNever smoked -4.543e+01 4.890e+01 -0.929 0.3543
Med.Statin.LLDyes -3.726e+01 3.474e+01 -1.073 0.2852
Med.all.antiplateletyes -3.984e+01 5.633e+01 -0.707 0.4805
GFR_MDRD -5.123e-01 9.494e-01 -0.540 0.5903
BMI -9.806e-01 4.072e+00 -0.241 0.8100
MedHx_CVDNo -4.176e+01 3.312e+01 -1.261 0.2094
stenose50-70% 1.348e+02 2.266e+02 0.595 0.5527
stenose70-90% 1.624e+02 1.969e+02 0.825 0.4109
stenose90-99% 1.704e+02 1.965e+02 0.867 0.3874
stenose100% (Occlusion) 8.305e+01 2.480e+02 0.335 0.7382
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 188.9 on 146 degrees of freedom
Multiple R-squared: 0.06428, Adjusted R-squared: -0.04467
F-statistic: 0.59 on 17 and 146 DF, p-value: 0.8954
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL6_rank
Effect size...............: -12.05388
Standard error............: 15.27504
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 58538544
T-value...................: -0.789123
P-value...................: 0.4313199
R^2.......................: 0.06428
Adjusted r^2..............: -0.044673
Sample size of AE DB......: 622
Sample size of model......: 164
Missing data %............: 73.63344
- processing IL8_rankfilter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch +
MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch MedHx_CVDNo
-689.53500 25.03377 0.06852 -50.67884
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-220.34 -110.86 -49.13 48.42 861.74
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -960.00274 698.95086 -1.373 0.1719
currentDF[, TRAIT] 29.63236 16.89164 1.754 0.0816 .
Age 0.23306 2.28606 0.102 0.9189
Gendermale -29.00854 40.81907 -0.711 0.4785
ORdate_epoch 0.08472 0.04652 1.821 0.0708 .
Hypertension.compositeyes 5.68117 50.63307 0.112 0.9108
DiabetesStatusDiabetes -5.42485 42.89174 -0.126 0.8995
SmokerStatusEx-smoker 0.54408 38.32077 0.014 0.9887
SmokerStatusNever smoked -56.86648 55.15094 -1.031 0.3043
Med.Statin.LLDyes -28.16969 36.69756 -0.768 0.4440
Med.all.antiplateletyes -13.73557 56.53932 -0.243 0.8084
GFR_MDRD -0.54721 0.90424 -0.605 0.5461
BMI -0.50697 4.32054 -0.117 0.9068
MedHx_CVDNo -42.78600 35.77129 -1.196 0.2337
stenose50-70% 154.03855 234.25201 0.658 0.5119
stenose70-90% 135.37351 204.57953 0.662 0.5093
stenose90-99% 177.02615 203.83540 0.868 0.3867
stenose100% (Occlusion) 128.02218 291.00996 0.440 0.6607
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 195.6 on 136 degrees of freedom
Multiple R-squared: 0.09426, Adjusted R-squared: -0.01895
F-statistic: 0.8326 on 17 and 136 DF, p-value: 0.6534
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL8_rank
Effect size...............: 29.63236
Standard error............: 16.89164
Odds ratio (effect size)..: 7.398963e+12
Lower 95% CI..............: 0.031
Upper 95% CI..............: 1.768585e+27
T-value...................: 1.754262
P-value...................: 0.08163803
R^2.......................: 0.094262
Adjusted r^2..............: -0.018955
Sample size of AE DB......: 622
Sample size of model......: 154
Missing data %............: 75.24116
- processing IL9_rankfilter: removed 436 rows (70%), 186 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-596.89466 27.36194 0.05932
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-267.13 -105.46 -53.02 30.86 876.34
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -898.55787 583.93956 -1.539 0.1257
currentDF[, TRAIT] 28.82261 14.70503 1.960 0.0516 .
Age 0.68701 2.14016 0.321 0.7486
Gendermale -7.26807 35.47764 -0.205 0.8379
ORdate_epoch 0.07349 0.03826 1.921 0.0564 .
Hypertension.compositeyes 30.90120 47.90383 0.645 0.5198
DiabetesStatusDiabetes -14.37190 39.65114 -0.362 0.7175
SmokerStatusEx-smoker -14.76794 34.65873 -0.426 0.6706
SmokerStatusNever smoked -49.76201 44.13368 -1.128 0.2611
Med.Statin.LLDyes -6.58869 35.04578 -0.188 0.8511
Med.all.antiplateletyes -27.92922 57.93817 -0.482 0.6304
GFR_MDRD 0.03106 0.80625 0.039 0.9693
BMI -1.15085 3.90391 -0.295 0.7685
MedHx_CVDNo -27.10743 32.03388 -0.846 0.3986
stenose50-70% 88.53558 249.32085 0.355 0.7230
stenose70-90% 140.44525 202.04417 0.695 0.4879
stenose90-99% 146.53001 201.19315 0.728 0.4674
stenose100% (Occlusion) 95.84826 255.92300 0.375 0.7085
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 195.4 on 168 degrees of freedom
Multiple R-squared: 0.05822, Adjusted R-squared: -0.03707
F-statistic: 0.611 on 17 and 168 DF, p-value: 0.8803
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL9_rank
Effect size...............: 28.82261
Standard error............: 14.70503
Odds ratio (effect size)..: 3.292306e+12
Lower 95% CI..............: 1.001
Upper 95% CI..............: 1.083114e+25
T-value...................: 1.960051
P-value...................: 0.05164397
R^2.......................: 0.058224
Adjusted r^2..............: -0.037075
Sample size of AE DB......: 622
Sample size of model......: 186
Missing data %............: 70.09646
- processing IL10_rankfilter: removed 483 rows (78%), 139 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + MedHx_CVD,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch MedHx_CVDNo
-1.073e+03 9.957e-02 -5.351e+01
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-231.23 -119.40 -56.07 33.06 878.98
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -957.88808 799.16361 -1.199 0.2330
currentDF[, TRAIT] -3.23438 19.40376 -0.167 0.8679
Age -0.56358 2.60731 -0.216 0.8292
Gendermale -7.51328 45.95202 -0.164 0.8704
ORdate_epoch 0.10082 0.05592 1.803 0.0739 .
Hypertension.compositeyes 7.60188 60.75392 0.125 0.9006
DiabetesStatusDiabetes 0.39277 50.18812 0.008 0.9938
SmokerStatusEx-smoker 0.82623 42.42096 0.019 0.9845
SmokerStatusNever smoked -27.32161 59.03562 -0.463 0.6443
Med.Statin.LLDyes -22.20267 42.30613 -0.525 0.6007
Med.all.antiplateletyes -24.56587 65.76869 -0.374 0.7094
GFR_MDRD -0.77674 1.20579 -0.644 0.5207
BMI -2.79626 5.95679 -0.469 0.6396
MedHx_CVDNo -49.98464 41.61717 -1.201 0.2321
stenose70-90% 80.84853 160.51978 0.504 0.6154
stenose90-99% 74.13248 159.23797 0.466 0.6424
stenose100% (Occlusion) -9.23424 232.90917 -0.040 0.9684
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 210.9 on 122 degrees of freedom
Multiple R-squared: 0.05361, Adjusted R-squared: -0.07051
F-statistic: 0.4319 on 16 and 122 DF, p-value: 0.9713
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL10_rank
Effect size...............: -3.234382
Standard error............: 19.40376
Odds ratio (effect size)..: 0.039
Lower 95% CI..............: 0
Upper 95% CI..............: 1.294608e+15
T-value...................: -0.166688
P-value...................: 0.8678911
R^2.......................: 0.053608
Adjusted r^2..............: -0.070509
Sample size of AE DB......: 622
Sample size of model......: 139
Missing data %............: 77.65273
- processing IL12_rankfilter: removed 476 rows (77%), 146 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + MedHx_CVD,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch MedHx_CVDNo
-1.055e+03 9.834e-02 -5.728e+01
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-227.09 -108.18 -54.87 26.20 842.86
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -841.05398 753.35447 -1.116 0.2663
currentDF[, TRAIT] -11.32035 18.32743 -0.618 0.5379
Age -1.36833 2.52837 -0.541 0.5893
Gendermale -6.39497 43.34574 -0.148 0.8829
ORdate_epoch 0.10030 0.05246 1.912 0.0581 .
Hypertension.compositeyes 5.71265 58.95456 0.097 0.9230
DiabetesStatusDiabetes -11.50397 46.96924 -0.245 0.8069
SmokerStatusEx-smoker -4.67117 41.68385 -0.112 0.9109
SmokerStatusNever smoked -36.54417 55.31040 -0.661 0.5100
Med.Statin.LLDyes -44.49186 41.48987 -1.072 0.2856
Med.all.antiplateletyes -33.25874 63.05851 -0.527 0.5988
GFR_MDRD -0.68665 1.11601 -0.615 0.5395
BMI -3.84222 5.64708 -0.680 0.4975
MedHx_CVDNo -59.63309 38.04154 -1.568 0.1194
stenose70-90% 84.87555 154.41154 0.550 0.5835
stenose90-99% 73.88707 153.79462 0.480 0.6317
stenose100% (Occlusion) -1.65105 270.55383 -0.006 0.9951
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 203.9 on 129 degrees of freedom
Multiple R-squared: 0.06562, Adjusted R-squared: -0.05028
F-statistic: 0.5662 on 16 and 129 DF, p-value: 0.904
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL12_rank
Effect size...............: -11.32035
Standard error............: 18.32743
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 48334895675
T-value...................: -0.617672
P-value...................: 0.5378801
R^2.......................: 0.065616
Adjusted r^2..............: -0.050277
Sample size of AE DB......: 622
Sample size of model......: 146
Missing data %............: 76.52733
- processing IL13_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-536.63910 20.70733 0.05432
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-254.34 -101.93 -58.19 18.77 887.91
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -815.88849 562.64459 -1.450 0.1487
currentDF[, TRAIT] 25.38908 14.49443 1.752 0.0815 .
Age 1.14661 2.00090 0.573 0.5673
Gendermale -15.03724 33.55999 -0.448 0.6546
ORdate_epoch 0.06771 0.03733 1.814 0.0713 .
Hypertension.compositeyes 19.01206 43.82387 0.434 0.6649
DiabetesStatusDiabetes -16.67881 36.28152 -0.460 0.6463
SmokerStatusEx-smoker -19.45102 32.73371 -0.594 0.5531
SmokerStatusNever smoked -54.72292 42.84052 -1.277 0.2031
Med.Statin.LLDyes -11.47693 32.54376 -0.353 0.7247
Med.all.antiplateletyes -44.43714 50.67578 -0.877 0.3817
GFR_MDRD -0.08118 0.77576 -0.105 0.9168
BMI -0.36063 3.63115 -0.099 0.9210
MedHx_CVDNo -16.42362 29.66718 -0.554 0.5805
stenose50-70% 76.71781 229.97245 0.334 0.7391
stenose70-90% 120.40380 199.09274 0.605 0.5461
stenose90-99% 121.67135 198.86903 0.612 0.5414
stenose100% (Occlusion) 61.19972 249.31790 0.245 0.8064
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 192.6 on 184 degrees of freedom
Multiple R-squared: 0.051, Adjusted R-squared: -0.03668
F-statistic: 0.5816 on 17 and 184 DF, p-value: 0.9028
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL13_rank
Effect size...............: 25.38908
Standard error............: 14.49443
Odds ratio (effect size)..: 106251900805
Lower 95% CI..............: 0.049
Upper 95% CI..............: 2.313369e+23
T-value...................: 1.751644
P-value...................: 0.08150119
R^2.......................: 0.050997
Adjusted r^2..............: -0.036682
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing IL21_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-549.48717 19.99262 0.05535
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-244.11 -107.14 -57.03 19.51 890.57
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -813.74393 563.14353 -1.445 0.1502
currentDF[, TRAIT] 24.30767 14.38887 1.689 0.0928 .
Age 0.95641 1.98423 0.482 0.6304
Gendermale -15.81199 33.64066 -0.470 0.6389
ORdate_epoch 0.06924 0.03727 1.858 0.0648 .
Hypertension.compositeyes 15.95207 43.73838 0.365 0.7157
DiabetesStatusDiabetes -17.94458 36.25035 -0.495 0.6212
SmokerStatusEx-smoker -16.69040 32.55548 -0.513 0.6088
SmokerStatusNever smoked -52.51493 42.67713 -1.231 0.2201
Med.Statin.LLDyes -12.29014 32.61252 -0.377 0.7067
Med.all.antiplateletyes -45.55642 50.73532 -0.898 0.3704
GFR_MDRD -0.12784 0.77564 -0.165 0.8693
BMI -0.52314 3.63018 -0.144 0.8856
MedHx_CVDNo -17.09252 29.66573 -0.576 0.5652
stenose50-70% 83.20392 229.83310 0.362 0.7178
stenose70-90% 123.64240 199.11105 0.621 0.5354
stenose90-99% 124.73424 198.87659 0.627 0.5313
stenose100% (Occlusion) 52.96675 249.50083 0.212 0.8321
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 192.7 on 184 degrees of freedom
Multiple R-squared: 0.04991, Adjusted R-squared: -0.03787
F-statistic: 0.5686 on 17 and 184 DF, p-value: 0.9118
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IL21_rank
Effect size...............: 24.30767
Standard error............: 14.38887
Odds ratio (effect size)..: 36031759894
Lower 95% CI..............: 0.02
Upper 95% CI..............: 6.378754e+22
T-value...................: 1.689339
P-value...................: 0.0928483
R^2.......................: 0.049909
Adjusted r^2..............: -0.037872
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing INFG_rankfilter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-934.2282 0.0871
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-207.59 -119.59 -55.32 29.56 887.61
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.130e+03 7.301e+02 -1.548 0.1239
currentDF[, TRAIT] 2.254e+00 1.890e+01 0.119 0.9053
Age -8.773e-01 2.407e+00 -0.365 0.7160
Gendermale -1.103e+01 4.392e+01 -0.251 0.8022
ORdate_epoch 1.056e-01 4.939e-02 2.137 0.0344 *
Hypertension.compositeyes 2.758e+00 5.818e+01 0.047 0.9623
DiabetesStatusDiabetes -2.604e+01 4.418e+01 -0.589 0.5565
SmokerStatusEx-smoker 1.009e+01 3.979e+01 0.254 0.8002
SmokerStatusNever smoked -3.233e+01 5.409e+01 -0.598 0.5510
Med.Statin.LLDyes -2.913e+01 3.933e+01 -0.741 0.4602
Med.all.antiplateletyes -5.108e+00 5.765e+01 -0.089 0.9295
GFR_MDRD -5.285e-01 9.724e-01 -0.544 0.5876
BMI -2.314e+00 4.492e+00 -0.515 0.6074
MedHx_CVDNo -3.591e+01 3.792e+01 -0.947 0.3453
stenose50-70% 1.378e+02 2.434e+02 0.566 0.5722
stenose70-90% 1.745e+02 2.124e+02 0.822 0.4128
stenose90-99% 1.754e+02 2.112e+02 0.830 0.4077
stenose100% (Occlusion) 1.188e+02 3.002e+02 0.396 0.6929
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 202.6 on 136 degrees of freedom
Multiple R-squared: 0.05546, Adjusted R-squared: -0.0626
F-statistic: 0.4698 on 17 and 136 DF, p-value: 0.9625
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: INFG_rank
Effect size...............: 2.253633
Standard error............: 18.90395
Odds ratio (effect size)..: 9.522
Lower 95% CI..............: 0
Upper 95% CI..............: 1.175199e+17
T-value...................: 0.119215
P-value...................: 0.905281
R^2.......................: 0.055462
Adjusted r^2..............: -0.062605
Sample size of AE DB......: 622
Sample size of model......: 154
Missing data %............: 75.24116
- processing TNFA_rankfilter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + MedHx_CVD,
data = currentDF)
Coefficients:
(Intercept) ORdate_epoch MedHx_CVDNo
-967.0629 0.0914 -59.6982
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-221.73 -117.61 -53.31 52.69 823.85
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.084e+02 7.288e+02 -1.246 0.2149
currentDF[, TRAIT] -2.753e+01 1.833e+01 -1.502 0.1356
Age 1.748e-04 2.476e+00 0.000 0.9999
Gendermale -1.527e+01 4.325e+01 -0.353 0.7246
ORdate_epoch 9.330e-02 5.079e-02 1.837 0.0685 .
Hypertension.compositeyes 2.746e+01 5.796e+01 0.474 0.6365
DiabetesStatusDiabetes -5.760e-01 4.680e+01 -0.012 0.9902
SmokerStatusEx-smoker 7.410e+00 4.035e+01 0.184 0.8546
SmokerStatusNever smoked -6.431e+01 5.589e+01 -1.151 0.2520
Med.Statin.LLDyes -4.758e+01 4.157e+01 -1.144 0.2546
Med.all.antiplateletyes -1.585e+01 6.239e+01 -0.254 0.7999
GFR_MDRD 1.046e-01 1.075e+00 0.097 0.9226
BMI -4.790e+00 5.324e+00 -0.900 0.3699
MedHx_CVDNo -6.784e+01 3.913e+01 -1.734 0.0854 .
stenose70-90% 8.055e+01 1.546e+02 0.521 0.6032
stenose90-99% 8.474e+01 1.530e+02 0.554 0.5807
stenose100% (Occlusion) 2.481e+01 2.696e+02 0.092 0.9268
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 204.4 on 128 degrees of freedom
Multiple R-squared: 0.08146, Adjusted R-squared: -0.03336
F-statistic: 0.7095 on 16 and 128 DF, p-value: 0.7803
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: TNFA_rank
Effect size...............: -27.52585
Standard error............: 18.32789
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 4432.909
T-value...................: -1.501856
P-value...................: 0.1355973
R^2.......................: 0.081458
Adjusted r^2..............: -0.03336
Sample size of AE DB......: 622
Sample size of model......: 145
Missing data %............: 76.6881
- processing MIF_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-595.42666 0.05908
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-210.27 -106.97 -60.02 23.95 905.44
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.037e+03 6.132e+02 -1.691 0.0925 .
currentDF[, TRAIT] 1.108e+01 1.679e+01 0.660 0.5099
Age 5.325e-01 1.979e+00 0.269 0.7881
Gendermale -1.038e+01 3.368e+01 -0.308 0.7582
ORdate_epoch 8.714e-02 4.196e-02 2.077 0.0392 *
Hypertension.compositeyes 1.588e+01 4.428e+01 0.359 0.7202
DiabetesStatusDiabetes -2.090e+01 3.647e+01 -0.573 0.5673
SmokerStatusEx-smoker -8.847e+00 3.264e+01 -0.271 0.7867
SmokerStatusNever smoked -4.247e+01 4.248e+01 -1.000 0.3188
Med.Statin.LLDyes -8.048e+00 3.271e+01 -0.246 0.8059
Med.all.antiplateletyes -4.421e+01 5.119e+01 -0.864 0.3889
GFR_MDRD -4.513e-02 7.922e-01 -0.057 0.9546
BMI -7.256e-01 3.655e+00 -0.199 0.8428
MedHx_CVDNo -1.841e+01 2.988e+01 -0.616 0.5385
stenose50-70% 1.043e+02 2.311e+02 0.451 0.6523
stenose70-90% 1.341e+02 2.007e+02 0.668 0.5050
stenose90-99% 1.432e+02 2.003e+02 0.715 0.4757
stenose100% (Occlusion) 5.252e+01 2.514e+02 0.209 0.8347
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 194 on 184 degrees of freedom
Multiple R-squared: 0.03745, Adjusted R-squared: -0.05148
F-statistic: 0.4211 on 17 and 184 DF, p-value: 0.9792
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MIF_rank
Effect size...............: 11.08372
Standard error............: 16.78661
Odds ratio (effect size)..: 65102.61
Lower 95% CI..............: 0
Upper 95% CI..............: 1.266626e+19
T-value...................: 0.660272
P-value...................: 0.5099053
R^2.......................: 0.037453
Adjusted r^2..............: -0.051478
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MCP1_rankfilter: removed 422 rows (68%), 200 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-579.67380 0.05789
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-216.43 -104.79 -55.81 17.15 914.36
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -959.34496 583.55474 -1.644 0.1019
currentDF[, TRAIT] 8.99600 14.36496 0.626 0.5319
Age 0.81366 2.00538 0.406 0.6854
Gendermale -13.59195 34.19475 -0.397 0.6915
ORdate_epoch 0.07985 0.03862 2.068 0.0401 *
Hypertension.compositeyes 18.57258 44.46921 0.418 0.6767
DiabetesStatusDiabetes -22.53319 36.68397 -0.614 0.5398
SmokerStatusEx-smoker -11.29593 32.73943 -0.345 0.7305
SmokerStatusNever smoked -45.95087 42.88098 -1.072 0.2853
Med.Statin.LLDyes -6.94201 32.95857 -0.211 0.8334
Med.all.antiplateletyes -63.01939 54.29691 -1.161 0.2473
GFR_MDRD -0.12223 0.78637 -0.155 0.8767
BMI -0.32909 3.72199 -0.088 0.9296
MedHx_CVDNo -19.17199 30.10813 -0.637 0.5251
stenose50-70% 115.28600 231.12232 0.499 0.6185
stenose70-90% 142.04894 200.50951 0.708 0.4796
stenose90-99% 154.26184 199.80843 0.772 0.4411
stenose100% (Occlusion) 49.42882 252.46420 0.196 0.8450
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 194.4 on 182 degrees of freedom
Multiple R-squared: 0.04097, Adjusted R-squared: -0.04861
F-statistic: 0.4574 on 17 and 182 DF, p-value: 0.9681
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MCP1_rank
Effect size...............: 8.996001
Standard error............: 14.36496
Odds ratio (effect size)..: 8070.746
Lower 95% CI..............: 0
Upper 95% CI..............: 1.363379e+16
T-value...................: 0.626246
P-value...................: 0.5319382
R^2.......................: 0.040972
Adjusted r^2..............: -0.048607
Sample size of AE DB......: 622
Sample size of model......: 200
Missing data %............: 67.84566
- processing MIP1a_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-549.03607 20.72285 0.05538
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-238.26 -107.43 -56.34 30.35 901.59
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -816.41951 578.17760 -1.412 0.1598
currentDF[, TRAIT] 21.12199 14.84396 1.423 0.1566
Age 0.50235 2.05756 0.244 0.8074
Gendermale -9.40047 35.55001 -0.264 0.7918
ORdate_epoch 0.06917 0.03815 1.813 0.0716 .
Hypertension.compositeyes 29.73900 46.56903 0.639 0.5239
DiabetesStatusDiabetes -20.42486 38.39172 -0.532 0.5954
SmokerStatusEx-smoker -13.20276 34.40457 -0.384 0.7016
SmokerStatusNever smoked -48.30374 44.20580 -1.093 0.2761
Med.Statin.LLDyes -3.67667 34.34110 -0.107 0.9149
Med.all.antiplateletyes -36.34477 57.64677 -0.630 0.5292
GFR_MDRD 0.05182 0.80267 0.065 0.9486
BMI -0.93470 3.80107 -0.246 0.8061
MedHx_CVDNo -25.06317 31.87635 -0.786 0.4328
stenose50-70% 87.37067 249.99444 0.349 0.7272
stenose70-90% 124.33135 202.43618 0.614 0.5399
stenose90-99% 129.86983 202.10686 0.643 0.5214
stenose100% (Occlusion) 54.07043 254.89493 0.212 0.8323
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 195.2 on 171 degrees of freedom
Multiple R-squared: 0.0482, Adjusted R-squared: -0.04643
F-statistic: 0.5093 on 17 and 171 DF, p-value: 0.946
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MIP1a_rank
Effect size...............: 21.12198
Standard error............: 14.84396
Odds ratio (effect size)..: 1489915331
Lower 95% CI..............: 0
Upper 95% CI..............: 6.435722e+21
T-value...................: 1.422934
P-value...................: 0.1565769
R^2.......................: 0.048195
Adjusted r^2..............: -0.046428
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing RANTES_rankfilter: removed 424 rows (68%), 198 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-583.35357 0.05819
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-229.60 -109.76 -61.13 28.75 923.72
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.147e+03 6.068e+02 -1.890 0.0604 .
currentDF[, TRAIT] 2.338e+01 1.592e+01 1.468 0.1438
Age 1.037e+00 2.026e+00 0.512 0.6095
Gendermale -1.738e+01 3.467e+01 -0.501 0.6167
ORdate_epoch 9.601e-02 4.091e-02 2.347 0.0200 *
Hypertension.compositeyes 1.834e+01 4.598e+01 0.399 0.6905
DiabetesStatusDiabetes -1.187e+01 3.748e+01 -0.317 0.7519
SmokerStatusEx-smoker -6.413e+00 3.344e+01 -0.192 0.8482
SmokerStatusNever smoked -4.695e+01 4.325e+01 -1.086 0.2791
Med.Statin.LLDyes -6.740e+00 3.318e+01 -0.203 0.8393
Med.all.antiplateletyes -5.342e+01 5.412e+01 -0.987 0.3250
GFR_MDRD -1.706e-02 7.909e-01 -0.022 0.9828
BMI -1.374e+00 3.736e+00 -0.368 0.7135
MedHx_CVDNo -1.069e+01 3.102e+01 -0.345 0.7308
stenose50-70% 1.100e+02 2.475e+02 0.445 0.6572
stenose70-90% 1.170e+02 2.019e+02 0.579 0.5630
stenose90-99% 1.312e+02 2.009e+02 0.653 0.5147
stenose100% (Occlusion) 1.434e+01 2.547e+02 0.056 0.9551
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 194.9 on 180 degrees of freedom
Multiple R-squared: 0.04709, Adjusted R-squared: -0.0429
F-statistic: 0.5233 on 17 and 180 DF, p-value: 0.939
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: RANTES_rank
Effect size...............: 23.3762
Standard error............: 15.92046
Odds ratio (effect size)..: 14195656897
Lower 95% CI..............: 0
Upper 95% CI..............: 5.057374e+23
T-value...................: 1.468312
P-value...................: 0.1437648
R^2.......................: 0.047092
Adjusted r^2..............: -0.042905
Sample size of AE DB......: 622
Sample size of model......: 198
Missing data %............: 68.1672
- processing MIG_rankfilter: removed 423 rows (68%), 199 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
143.24 33.68
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-273.62 -104.85 -52.65 21.30 886.05
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -529.41864 581.78088 -0.910 0.3640
currentDF[, TRAIT] 36.35714 15.64862 2.323 0.0213 *
Age 1.26423 1.99470 0.634 0.5270
Gendermale -20.84024 33.85373 -0.616 0.5389
ORdate_epoch 0.04692 0.03913 1.199 0.2320
Hypertension.compositeyes 13.53763 44.88270 0.302 0.7633
DiabetesStatusDiabetes -22.39565 36.71506 -0.610 0.5426
SmokerStatusEx-smoker -23.11434 32.84319 -0.704 0.4825
SmokerStatusNever smoked -58.41474 42.97676 -1.359 0.1758
Med.Statin.LLDyes -9.08412 32.84828 -0.277 0.7824
Med.all.antiplateletyes -49.73774 51.63087 -0.963 0.3367
GFR_MDRD -0.26978 0.77573 -0.348 0.7284
BMI -0.59644 3.62277 -0.165 0.8694
MedHx_CVDNo -19.21883 30.06850 -0.639 0.5235
stenose50-70% 78.20209 245.23646 0.319 0.7502
stenose70-90% 122.95937 198.34934 0.620 0.5361
stenose90-99% 121.73267 197.89572 0.615 0.5392
stenose100% (Occlusion) 44.82830 249.20861 0.180 0.8574
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 191.9 on 181 degrees of freedom
Multiple R-squared: 0.06574, Adjusted R-squared: -0.02201
F-statistic: 0.7492 on 17 and 181 DF, p-value: 0.7487
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MIG_rank
Effect size...............: 36.35714
Standard error............: 15.64862
Odds ratio (effect size)..: 6.161764e+15
Lower 95% CI..............: 294.665
Upper 95% CI..............: 1.288491e+29
T-value...................: 2.323344
P-value...................: 0.02127203
R^2.......................: 0.065742
Adjusted r^2..............: -0.022006
Sample size of AE DB......: 622
Sample size of model......: 199
Missing data %............: 68.00643
- processing IP10_rankfilter: removed 439 rows (71%), 183 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-680.4241 24.9911 0.0659
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-247.73 -98.16 -52.71 30.53 889.41
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.020e+03 5.864e+02 -1.740 0.0838 .
currentDF[, TRAIT] 2.973e+01 1.570e+01 1.894 0.0600 .
Age 8.596e-01 2.143e+00 0.401 0.6888
Gendermale 3.446e+00 3.517e+01 0.098 0.9221
ORdate_epoch 8.172e-02 3.858e-02 2.118 0.0356 *
Hypertension.compositeyes 3.785e+01 4.762e+01 0.795 0.4279
DiabetesStatusDiabetes -2.617e+01 3.990e+01 -0.656 0.5128
SmokerStatusEx-smoker -2.075e+01 3.550e+01 -0.584 0.5598
SmokerStatusNever smoked -4.930e+01 4.573e+01 -1.078 0.2825
Med.Statin.LLDyes 4.238e+00 3.520e+01 0.120 0.9043
Med.all.antiplateletyes -2.227e+01 5.452e+01 -0.408 0.6835
GFR_MDRD -2.714e-02 8.371e-01 -0.032 0.9742
BMI -1.448e+00 3.791e+00 -0.382 0.7030
MedHx_CVDNo -3.487e+01 3.222e+01 -1.082 0.2808
stenose50-70% 1.009e+02 2.507e+02 0.402 0.6879
stenose70-90% 1.355e+02 2.034e+02 0.666 0.5062
stenose90-99% 1.452e+02 2.023e+02 0.718 0.4740
stenose100% (Occlusion) 1.146e+02 2.555e+02 0.448 0.6545
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 196 on 165 degrees of freedom
Multiple R-squared: 0.06603, Adjusted R-squared: -0.0302
F-statistic: 0.6862 on 17 and 165 DF, p-value: 0.8135
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: IP10_rank
Effect size...............: 29.72744
Standard error............: 15.69949
Odds ratio (effect size)..: 8.136947e+12
Lower 95% CI..............: 0.352
Upper 95% CI..............: 1.879912e+26
T-value...................: 1.893529
P-value...................: 0.0600379
R^2.......................: 0.066029
Adjusted r^2..............: -0.030199
Sample size of AE DB......: 622
Sample size of model......: 183
Missing data %............: 70.57878
- processing Eotaxin1_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-595.42666 0.05908
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-225.60 -106.48 -59.53 24.37 909.93
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -769.00897 571.49265 -1.346 0.1801
currentDF[, TRAIT] 17.57292 14.65565 1.199 0.2320
Age 0.75068 1.98529 0.378 0.7058
Gendermale -14.39509 33.81495 -0.426 0.6708
ORdate_epoch 0.06588 0.03798 1.735 0.0845 .
Hypertension.compositeyes 12.28269 43.85703 0.280 0.7797
DiabetesStatusDiabetes -19.19468 36.39179 -0.527 0.5985
SmokerStatusEx-smoker -14.38968 32.63517 -0.441 0.6598
SmokerStatusNever smoked -48.57383 42.80144 -1.135 0.2579
Med.Statin.LLDyes -9.34473 32.64418 -0.286 0.7750
Med.all.antiplateletyes -44.94931 50.95582 -0.882 0.3789
GFR_MDRD -0.11253 0.77879 -0.144 0.8853
BMI -0.48609 3.64590 -0.133 0.8941
MedHx_CVDNo -17.41960 29.79750 -0.585 0.5595
stenose50-70% 96.84701 230.43388 0.420 0.6748
stenose70-90% 129.74264 199.88127 0.649 0.5171
stenose90-99% 133.51770 199.66989 0.669 0.5045
stenose100% (Occlusion) 46.59851 250.68092 0.186 0.8527
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 193.4 on 184 degrees of freedom
Multiple R-squared: 0.04265, Adjusted R-squared: -0.0458
F-statistic: 0.4822 on 17 and 184 DF, p-value: 0.9586
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 17.57292
Standard error............: 14.65565
Odds ratio (effect size)..: 42837496
Lower 95% CI..............: 0
Upper 95% CI..............: 1.27927e+20
T-value...................: 1.199055
P-value...................: 0.2320485
R^2.......................: 0.042653
Adjusted r^2..............: -0.045797
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing TARC_rankfilter: removed 444 rows (71%), 178 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
154.1
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-262.76 -109.02 -59.47 31.29 898.19
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.067e+03 7.149e+02 -1.492 0.1377
currentDF[, TRAIT] 2.768e+01 1.712e+01 1.617 0.1079
Age 1.568e+00 2.264e+00 0.692 0.4897
Gendermale -8.352e+00 3.817e+01 -0.219 0.8271
ORdate_epoch 8.345e-02 4.959e-02 1.683 0.0944 .
Hypertension.compositeyes 2.264e+01 4.811e+01 0.471 0.6386
DiabetesStatusDiabetes -2.275e+01 4.027e+01 -0.565 0.5729
SmokerStatusEx-smoker -1.347e+01 3.654e+01 -0.369 0.7129
SmokerStatusNever smoked -5.601e+01 4.669e+01 -1.200 0.2320
Med.Statin.LLDyes -1.946e+01 3.800e+01 -0.512 0.6092
Med.all.antiplateletyes -4.822e+01 5.889e+01 -0.819 0.4141
GFR_MDRD 1.206e-01 8.818e-01 0.137 0.8914
BMI -6.812e-01 4.072e+00 -0.167 0.8673
MedHx_CVDNo -2.016e+01 3.353e+01 -0.601 0.5485
stenose50-70% 8.714e+01 2.575e+02 0.338 0.7355
stenose70-90% 1.520e+02 2.104e+02 0.723 0.4710
stenose90-99% 1.432e+02 2.097e+02 0.683 0.4957
stenose100% (Occlusion) 6.627e+01 2.670e+02 0.248 0.8043
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 203.3 on 160 degrees of freedom
Multiple R-squared: 0.04682, Adjusted R-squared: -0.05446
F-statistic: 0.4623 on 17 and 160 DF, p-value: 0.966
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: TARC_rank
Effect size...............: 27.67735
Standard error............: 17.11956
Odds ratio (effect size)..: 1.047421e+12
Lower 95% CI..............: 0.003
Upper 95% CI..............: 3.913691e+26
T-value...................: 1.616709
P-value...................: 0.107911
R^2.......................: 0.046816
Adjusted r^2..............: -0.05446
Sample size of AE DB......: 622
Sample size of model......: 178
Missing data %............: 71.38264
- processing PARC_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-839.60679 20.70565 0.07849
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-224.93 -103.98 -55.73 21.66 877.98
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.236e+03 6.081e+02 -2.033 0.0435 *
currentDF[, TRAIT] 2.383e+01 1.547e+01 1.540 0.1252
Age 6.982e-01 1.970e+00 0.354 0.7234
Gendermale -7.589e+00 3.348e+01 -0.227 0.8209
ORdate_epoch 9.871e-02 4.036e-02 2.446 0.0154 *
Hypertension.compositeyes 2.303e+01 4.428e+01 0.520 0.6036
DiabetesStatusDiabetes -1.893e+01 3.627e+01 -0.522 0.6023
SmokerStatusEx-smoker -1.239e+01 3.241e+01 -0.382 0.7027
SmokerStatusNever smoked -4.993e+01 4.257e+01 -1.173 0.2424
Med.Statin.LLDyes -9.246e+00 3.254e+01 -0.284 0.7766
Med.all.antiplateletyes -3.661e+01 5.083e+01 -0.720 0.4723
GFR_MDRD 2.138e-02 7.831e-01 0.027 0.9782
BMI -7.219e-02 3.654e+00 -0.020 0.9843
MedHx_CVDNo -2.125e+01 2.968e+01 -0.716 0.4751
stenose50-70% 1.151e+02 2.293e+02 0.502 0.6164
stenose70-90% 1.397e+02 1.990e+02 0.702 0.4835
stenose90-99% 1.541e+02 1.982e+02 0.777 0.4380
stenose100% (Occlusion) 9.042e+01 2.505e+02 0.361 0.7186
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 193 on 184 degrees of freedom
Multiple R-squared: 0.04746, Adjusted R-squared: -0.04055
F-statistic: 0.5392 on 17 and 184 DF, p-value: 0.9302
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: PARC_rank
Effect size...............: 23.83311
Standard error............: 15.4727
Odds ratio (effect size)..: 22417658328
Lower 95% CI..............: 0.002
Upper 95% CI..............: 3.320626e+23
T-value...................: 1.540333
P-value...................: 0.1251972
R^2.......................: 0.047455
Adjusted r^2..............: -0.040551
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MDC_rankfilter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-783.10023 23.39734 0.07401
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-245.50 -107.44 -51.53 39.01 884.38
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.082e+03 5.946e+02 -1.820 0.0706 .
currentDF[, TRAIT] 2.440e+01 1.563e+01 1.561 0.1204
Age 4.990e-01 2.048e+00 0.244 0.8078
Gendermale -6.010e+00 3.527e+01 -0.170 0.8649
ORdate_epoch 8.900e-02 3.969e-02 2.242 0.0262 *
Hypertension.compositeyes 3.108e+01 4.648e+01 0.669 0.5045
DiabetesStatusDiabetes -1.683e+01 3.866e+01 -0.435 0.6639
SmokerStatusEx-smoker -7.226e+00 3.418e+01 -0.211 0.8328
SmokerStatusNever smoked -4.683e+01 4.401e+01 -1.064 0.2888
Med.Statin.LLDyes -9.263e+00 3.468e+01 -0.267 0.7897
Med.all.antiplateletyes -2.854e+01 5.770e+01 -0.495 0.6215
GFR_MDRD 7.812e-02 7.983e-01 0.098 0.9222
BMI -9.612e-01 3.814e+00 -0.252 0.8013
MedHx_CVDNo -2.471e+01 3.182e+01 -0.777 0.4385
stenose50-70% 8.087e+01 2.498e+02 0.324 0.7465
stenose70-90% 1.273e+02 2.018e+02 0.631 0.5290
stenose90-99% 1.357e+02 2.011e+02 0.675 0.5008
stenose100% (Occlusion) 6.229e+01 2.543e+02 0.245 0.8068
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 194.9 on 171 degrees of freedom
Multiple R-squared: 0.04959, Adjusted R-squared: -0.04489
F-statistic: 0.5249 on 17 and 171 DF, p-value: 0.9379
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MDC_rank
Effect size...............: 24.39727
Standard error............: 15.62908
Odds ratio (effect size)..: 39409527322
Lower 95% CI..............: 0.002
Upper 95% CI..............: 7.931194e+23
T-value...................: 1.561018
P-value...................: 0.1203682
R^2.......................: 0.049592
Adjusted r^2..............: -0.044893
Sample size of AE DB......: 622
Sample size of model......: 189
Missing data %............: 69.61415
- processing OPG_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-595.42666 0.05908
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-204.96 -105.84 -57.32 15.11 919.96
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -898.00441 566.83367 -1.584 0.1149
currentDF[, TRAIT] 7.06770 14.17536 0.499 0.6187
Age 0.59262 2.00158 0.296 0.7675
Gendermale -10.32970 33.71851 -0.306 0.7597
ORdate_epoch 0.07493 0.03741 2.003 0.0467 *
Hypertension.compositeyes 14.36349 44.15906 0.325 0.7453
DiabetesStatusDiabetes -22.00132 36.41811 -0.604 0.5465
SmokerStatusEx-smoker -12.70657 32.89677 -0.386 0.6998
SmokerStatusNever smoked -42.63938 42.69875 -0.999 0.3193
Med.Statin.LLDyes -7.82172 32.73196 -0.239 0.8114
Med.all.antiplateletyes -43.72500 51.25315 -0.853 0.3947
GFR_MDRD -0.13372 0.78109 -0.171 0.8643
BMI -0.49530 3.66895 -0.135 0.8928
MedHx_CVDNo -19.70630 29.83714 -0.660 0.5098
stenose50-70% 112.40078 230.73343 0.487 0.6267
stenose70-90% 146.89802 200.10420 0.734 0.4638
stenose90-99% 156.87099 199.36270 0.787 0.4324
stenose100% (Occlusion) 70.41466 251.99829 0.279 0.7802
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 194.1 on 184 degrees of freedom
Multiple R-squared: 0.03647, Adjusted R-squared: -0.05255
F-statistic: 0.4097 on 17 and 184 DF, p-value: 0.982
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: OPG_rank
Effect size...............: 7.0677
Standard error............: 14.17536
Odds ratio (effect size)..: 1173.446
Lower 95% CI..............: 0
Upper 95% CI..............: 1.367017e+15
T-value...................: 0.498591
P-value...................: 0.6186634
R^2.......................: 0.036474
Adjusted r^2..............: -0.052547
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing sICAM1_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-595.42666 0.05908
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-208.90 -107.12 -59.45 18.29 922.87
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -874.98462 581.37306 -1.505 0.1340
currentDF[, TRAIT] -0.59945 14.91174 -0.040 0.9680
Age 0.40800 2.01473 0.203 0.8397
Gendermale -9.24000 33.69708 -0.274 0.7842
ORdate_epoch 0.07417 0.03862 1.920 0.0564 .
Hypertension.compositeyes 12.29809 44.27175 0.278 0.7815
DiabetesStatusDiabetes -22.58646 36.53333 -0.618 0.5372
SmokerStatusEx-smoker -10.34652 32.61779 -0.317 0.7514
SmokerStatusNever smoked -39.42007 42.98150 -0.917 0.3603
Med.Statin.LLDyes -6.97670 32.71859 -0.213 0.8314
Med.all.antiplateletyes -41.27648 51.09743 -0.808 0.4202
GFR_MDRD -0.13685 0.78532 -0.174 0.8618
BMI -0.65089 3.65892 -0.178 0.8590
MedHx_CVDNo -19.75893 30.05048 -0.658 0.5117
stenose50-70% 115.72191 231.04859 0.501 0.6171
stenose70-90% 146.34915 201.12651 0.728 0.4678
stenose90-99% 157.54215 200.31835 0.786 0.4326
stenose100% (Occlusion) 61.11278 251.79656 0.243 0.8085
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 194.2 on 184 degrees of freedom
Multiple R-squared: 0.03518, Adjusted R-squared: -0.05396
F-statistic: 0.3947 on 17 and 184 DF, p-value: 0.9853
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: sICAM1_rank
Effect size...............: -0.599451
Standard error............: 14.91174
Odds ratio (effect size)..: 0.549
Lower 95% CI..............: 0
Upper 95% CI..............: 2.708875e+12
T-value...................: -0.0402
P-value...................: 0.9679773
R^2.......................: 0.035181
Adjusted r^2..............: -0.05396
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing VEGFA_rankfilter: removed 445 rows (72%), 177 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-761.88307 0.07303
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-211.87 -112.75 -62.08 37.07 951.47
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -857.05064 674.67248 -1.270 0.2058
currentDF[, TRAIT] -7.51938 18.06444 -0.416 0.6778
Age 0.65319 2.36424 0.276 0.7827
Gendermale -24.22377 40.08486 -0.604 0.5465
ORdate_epoch 0.08736 0.05015 1.742 0.0834 .
Hypertension.compositeyes 15.89088 53.55795 0.297 0.7671
DiabetesStatusDiabetes -20.29456 43.00537 -0.472 0.6376
SmokerStatusEx-smoker -6.47541 38.23480 -0.169 0.8657
SmokerStatusNever smoked -47.34103 52.36518 -0.904 0.3673
Med.Statin.LLDyes -3.99001 38.43545 -0.104 0.9174
Med.all.antiplateletyes -31.51279 57.40857 -0.549 0.5838
GFR_MDRD -0.07063 0.84980 -0.083 0.9339
BMI -3.13452 4.39684 -0.713 0.4769
MedHx_CVDNo -8.99507 35.69762 -0.252 0.8014
stenose70-90% 21.24933 164.67280 0.129 0.8975
stenose90-99% 11.19314 163.13774 0.069 0.9454
stenose100% (Occlusion) -89.30165 232.24506 -0.385 0.7011
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 214.7 on 160 degrees of freedom
Multiple R-squared: 0.03975, Adjusted R-squared: -0.05628
F-statistic: 0.4139 on 16 and 160 DF, p-value: 0.9774
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: VEGFA_rank
Effect size...............: -7.51938
Standard error............: 18.06444
Odds ratio (effect size)..: 0.001
Lower 95% CI..............: 0
Upper 95% CI..............: 1.291638e+12
T-value...................: -0.416253
P-value...................: 0.6777827
R^2.......................: 0.039748
Adjusted r^2..............: -0.056277
Sample size of AE DB......: 622
Sample size of model......: 177
Missing data %............: 71.54341
- processing TGFB_rankfilter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-831.77203 0.07845
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-232.22 -106.11 -60.38 34.46 924.97
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.162e+03 5.947e+02 -1.954 0.0522 .
currentDF[, TRAIT] 1.841e+01 1.510e+01 1.220 0.2240
Age 9.471e-01 2.109e+00 0.449 0.6539
Gendermale -2.174e+01 3.538e+01 -0.614 0.5397
ORdate_epoch 9.468e-02 3.950e-02 2.397 0.0175 *
Hypertension.compositeyes 3.227e+01 4.599e+01 0.702 0.4838
DiabetesStatusDiabetes -2.667e+01 3.750e+01 -0.711 0.4778
SmokerStatusEx-smoker -1.043e+01 3.445e+01 -0.303 0.7625
SmokerStatusNever smoked -5.163e+01 4.570e+01 -1.130 0.2600
Med.Statin.LLDyes 1.823e+00 3.457e+01 0.053 0.9580
Med.all.antiplateletyes -4.266e+01 5.557e+01 -0.768 0.4436
GFR_MDRD 2.633e-01 8.244e-01 0.319 0.7498
BMI -1.775e+00 3.901e+00 -0.455 0.6497
MedHx_CVDNo -7.356e+00 3.155e+01 -0.233 0.8159
stenose50-70% 8.851e+01 2.456e+02 0.360 0.7190
stenose70-90% 1.395e+02 2.121e+02 0.658 0.5117
stenose90-99% 1.414e+02 2.114e+02 0.669 0.5044
stenose100% (Occlusion) 1.853e+01 2.682e+02 0.069 0.9450
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 205.3 on 185 degrees of freedom
Multiple R-squared: 0.05637, Adjusted R-squared: -0.03034
F-statistic: 0.6501 on 17 and 185 DF, p-value: 0.8479
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: TGFB_rank
Effect size...............: 18.415
Standard error............: 15.09506
Odds ratio (effect size)..: 99433083
Lower 95% CI..............: 0
Upper 95% CI..............: 7.025917e+20
T-value...................: 1.219935
P-value...................: 0.2240422
R^2.......................: 0.056371
Adjusted r^2..............: -0.030341
Sample size of AE DB......: 622
Sample size of model......: 203
Missing data %............: 67.36334
- processing MMP2_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-588.25470 0.05846
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-211.23 -103.08 -58.52 34.10 912.74
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -585.87301 570.97658 -1.026 0.306
currentDF[, TRAIT] -18.69568 14.57490 -1.283 0.201
Age -0.53589 1.94360 -0.276 0.783
Gendermale -17.27846 32.87773 -0.526 0.600
ORdate_epoch 0.06212 0.03769 1.648 0.101
Hypertension.compositeyes -8.19819 44.45142 -0.184 0.854
DiabetesStatusDiabetes -19.65852 35.59715 -0.552 0.581
SmokerStatusEx-smoker 7.65630 32.20298 0.238 0.812
SmokerStatusNever smoked -17.56917 42.52024 -0.413 0.680
Med.Statin.LLDyes -6.02004 31.98231 -0.188 0.851
Med.all.antiplateletyes -39.18601 50.21618 -0.780 0.436
GFR_MDRD -0.63804 0.74505 -0.856 0.393
BMI -1.66937 3.70511 -0.451 0.653
MedHx_CVDNo -23.53385 29.49536 -0.798 0.426
stenose50-70% 131.19782 227.35079 0.577 0.565
stenose70-90% 146.34361 197.36497 0.741 0.459
stenose90-99% 154.96431 196.51619 0.789 0.431
stenose100% (Occlusion) 46.28176 247.92739 0.187 0.852
Residual standard error: 191.1 on 184 degrees of freedom
Multiple R-squared: 0.04312, Adjusted R-squared: -0.04528
F-statistic: 0.4878 on 17 and 184 DF, p-value: 0.9563
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MMP2_rank
Effect size...............: -18.69568
Standard error............: 14.5749
Odds ratio (effect size)..: 0
Lower 95% CI..............: 0
Upper 95% CI..............: 19362.95
T-value...................: -1.282732
P-value...................: 0.2011996
R^2.......................: 0.043123
Adjusted r^2..............: -0.045284
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MMP8_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) ORdate_epoch
-588.25470 0.05846
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-206.32 -101.69 -55.24 27.81 918.24
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -673.66594 562.53501 -1.198 0.2326
currentDF[, TRAIT] 15.92602 14.09295 1.130 0.2599
Age -0.14160 1.93974 -0.073 0.9419
Gendermale -18.33864 33.13693 -0.553 0.5806
ORdate_epoch 0.06916 0.03713 1.863 0.0641 .
Hypertension.compositeyes 0.77161 43.92616 0.018 0.9860
DiabetesStatusDiabetes -15.41899 35.90461 -0.429 0.6681
SmokerStatusEx-smoker 6.59946 32.19447 0.205 0.8378
SmokerStatusNever smoked -24.57053 42.18095 -0.583 0.5609
Med.Statin.LLDyes -5.36658 32.01279 -0.168 0.8671
Med.all.antiplateletyes -40.49969 50.22443 -0.806 0.4211
GFR_MDRD -0.38323 0.74848 -0.512 0.6093
BMI -1.68055 3.71230 -0.453 0.6513
MedHx_CVDNo -19.14639 29.65915 -0.646 0.5194
stenose50-70% 82.36604 229.21388 0.359 0.7198
stenose70-90% 89.77532 202.14072 0.444 0.6575
stenose90-99% 105.21673 200.50132 0.525 0.6004
stenose100% (Occlusion) -6.11296 252.61424 -0.024 0.9807
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 191.3 on 184 degrees of freedom
Multiple R-squared: 0.04122, Adjusted R-squared: -0.04736
F-statistic: 0.4653 on 17 and 184 DF, p-value: 0.9653
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MMP8_rank
Effect size...............: 15.92602
Standard error............: 14.09295
Odds ratio (effect size)..: 8252462
Lower 95% CI..............: 0
Upper 95% CI..............: 8.17984e+18
T-value...................: 1.13007
P-value...................: 0.2599176
R^2.......................: 0.041221
Adjusted r^2..............: -0.047362
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412
- processing MMP9_rankfilter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-704.49683 24.59556 0.06777
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-215.50 -103.93 -54.74 35.05 903.41
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -854.24450 561.45505 -1.521 0.1299
currentDF[, TRAIT] 25.04696 13.84364 1.809 0.0720 .
Age 0.07510 1.93550 0.039 0.9691
Gendermale -15.80068 32.53664 -0.486 0.6278
ORdate_epoch 0.07906 0.03718 2.126 0.0348 *
Hypertension.compositeyes 14.51065 44.33455 0.327 0.7438
DiabetesStatusDiabetes -12.36375 35.71770 -0.346 0.7296
SmokerStatusEx-smoker 2.98272 31.98073 0.093 0.9258
SmokerStatusNever smoked -30.42857 42.06733 -0.723 0.4704
Med.Statin.LLDyes -7.67966 31.86230 -0.241 0.8098
Med.all.antiplateletyes -49.13710 49.99691 -0.983 0.3270
GFR_MDRD -0.27443 0.74736 -0.367 0.7139
BMI -1.63472 3.68341 -0.444 0.6577
MedHx_CVDNo -17.89552 29.46392 -0.607 0.5444
stenose50-70% 108.14932 226.09638 0.478 0.6330
stenose70-90% 121.42317 196.64276 0.617 0.5377
stenose90-99% 134.67853 195.77927 0.688 0.4924
stenose100% (Occlusion) 41.68767 246.86505 0.169 0.8661
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 190.2 on 184 degrees of freedom
Multiple R-squared: 0.05144, Adjusted R-squared: -0.0362
F-statistic: 0.587 on 17 and 184 DF, p-value: 0.899
Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: CD36
Trait/outcome.............: MMP9_rank
Effect size...............: 25.04696
Standard error............: 13.84364
Odds ratio (effect size)..: 75467110683
Lower 95% CI..............: 0.124
Upper 95% CI..............: 4.588854e+22
T-value...................: 1.809275
P-value...................: 0.07204037
R^2.......................: 0.051442
Adjusted r^2..............: -0.036197
Sample size of AE DB......: 622
Sample size of model......: 202
Missing data %............: 67.52412 Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)DT::datatable(GLM.results)
# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AERNASE.clin.Con.Multi.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)Target expression vs. vulnerability index
Here we plot the levels of inverse-rank normal transformed PCSK9
plaque levels from experiment 1 and 2 to the
Plaque vulnerability index.
library(sjlabelled)
AERNASE.clin$yeartemp <- as.numeric(year(AERNASE.clin$dateok))
attach(AERNASE.clin)
AERNASE.clin[,"ORyearGroup"] <- NA
AERNASE.clin$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AERNASE.clin$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AERNASE.clin)
table(AERNASE.clin$ORyearGroup, AERNASE.clin$ORdate_year)
No data available/missing 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
< 2007 0 31 61 66 82 85 67 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 2007 0 0 0 0 0 0 0 63 68 34 31 22 5 3 3 1 0 0 0 0 0 0Visualisations
PCSK9
# Global test
compare_means(PCSK9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin, method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin,
x = "Plaque_Vulnerability_Index",
y = "PCSK9",
xlab = "Plaque vulnerability index",
ylab = "PCSK9 (normalized expression)\n",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")p2 <- ggpubr::ggboxplot(AERNASE.clin,
x = "Plaque_Vulnerability_Index",
y = "PCSK9",
xlab = "Plaque vulnerability index by gender",
ylab = "PCSK9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
p5 <- ggpubr::ggboxplot(AERNASE.clin,
x = "Plaque_Vulnerability_Index",
y = "PCSK9",
xlab = "Plaque vulnerability index",
ylab = "PCSK9 (normalized expression)\n",
color = "Plaque_Vulnerability_Index",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
compare_means(PCSK9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")p6 <- ggpubr::ggboxplot(AERNASE.clin,
x = "Plaque_Vulnerability_Index",
y = "PCSK9",
xlab = "Plaque vulnerability index",
ylab = "PCSK9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex_Facet_byYear.byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
Model 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of the plaque vulnerability index as a function of plaque PCSK9 levels.
TRAITS.TARGET.RANK.extra = c("PCSK9")
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
PROTEIN = TRAITS.TARGET.RANK.extra[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "Plaque_Vulnerability_Index"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AERNASE.clin %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
droplevels(.)
# fix numeric OR year
# currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
# table(currentDF$ORdate_year)
### univariate
# + Hypertension.composite + DiabetesStatus + SmokerCurrent +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# CAD_history + Stroke_history + Peripheral.interv + stenose
# fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
# data = currentDF,
# Hess = TRUE)
fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch,
data = currentDF,
Hess = TRUE)
print(summary(fit))
## store table
(ctable <- coef(summary(fit)))
## calculate and store p values
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
## combined table
print((ctable <- cbind(ctable, "p value" = p)))
}
Analysis of PCSK9.
- processing Plaque_Vulnerability_Indexfilter: no rows removed
filter_if: no rows removed
Warning: NaNs producedCall:
polr(formula = currentDF[, TRAIT] ~ currentDF[, PROTEIN] + Age +
Gender + ORdate_epoch, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.1146763 0.0428127 2.679
Age 0.0147499 0.0004045 36.465
Gendermale 0.5792864 0.0031785 182.253
ORdate_epoch -0.0004569 NaN NaN
Intercepts:
Value Std. Error t value
0|1 -7.2796 0.0009 -8070.1471
1|2 -5.8208 0.0189 -307.9476
2|3 -4.6060 0.1027 -44.8328
3|4 -2.9527 0.1362 -21.6747
4|5 -1.6312 0.1398 -11.6711
Residual Deviance: 1955.379
AIC: 1973.379 Warning: NaNs produced Value Std. Error t value p value
currentDF[, PROTEIN] 0.1146762900 0.0428126827 2.678559 7.393974e-03
Age 0.0147498655 0.0004044891 36.465417 3.919741e-291
Gendermale 0.5792863925 0.0031784702 182.253209 0.000000e+00
ORdate_epoch -0.0004569398 NaN NaN NaN
0|1 -7.2796426168 0.0009020458 -8070.147119 0.000000e+00
1|2 -5.8207609043 0.0189017920 -307.947570 0.000000e+00
2|3 -4.6060057798 0.1027374981 -44.832762 0.000000e+00
3|4 -2.9526553462 0.1362259590 -21.674689 3.556283e-104
4|5 -1.6312457031 0.1397677819 -11.671114 1.790632e-31Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..
for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
PROTEIN = TRAITS.TARGET.RANK.extra[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "Plaque_Vulnerability_Index"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AERNASE.clin %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
droplevels(.)
# fix numeric OR year
# currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
# fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
# data = currentDF,
# Hess = TRUE)
fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
data = currentDF,
Hess = TRUE)
print(summary(fit))
## store table
(ctable <- coef(summary(fit)))
## calculate and store p values
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
## combined table
print((ctable <- cbind(ctable, "p value" = p)))
}
Analysis of PCSK9.
- processing Plaque_Vulnerability_Indexfilter: removed 80 rows (13%), 542 rows remaining
filter_if: no rows removed
Warning: NaNs producedCall:
polr(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 1.464e-01 4.671e-02 3.135e+00
Age 1.445e-02 5.845e-03 2.471e+00
Gendermale 6.045e-01 2.869e-03 2.107e+02
ORdate_epoch -5.483e-04 NaN NaN
Hypertension.compositeyes -6.123e-02 9.373e-04 -6.533e+01
DiabetesStatusDiabetes -1.615e-01 9.172e-04 -1.761e+02
SmokerStatusEx-smoker 1.172e-02 1.705e-03 6.873e+00
SmokerStatusNever smoked 3.697e-01 8.886e-04 4.161e+02
Med.Statin.LLDyes 6.653e-02 1.403e-03 4.743e+01
Med.all.antiplateletyes 1.757e-01 1.507e-03 1.166e+02
GFR_MDRD 1.022e-03 3.389e-03 3.015e-01
BMI -4.975e-02 1.645e-02 -3.025e+00
MedHx_CVDNo -2.365e-01 2.060e-03 -1.148e+02
stenose50-70% -4.023e-01 3.173e-04 -1.268e+03
stenose70-90% -7.911e-01 2.693e-03 -2.938e+02
stenose90-99% -1.157e+00 2.578e-03 -4.487e+02
stenose100% (Occlusion) -1.592e+00 7.429e-05 -2.142e+04
stenose50-99% -2.842e+01 2.886e-11 -9.845e+11
Intercepts:
Value Std. Error t value
0|1 -1.062130e+01 5.000000e-04 -1.972118e+04
1|2 -9.117700e+00 2.210000e-02 -4.120315e+02
2|3 -7.889500e+00 1.127000e-01 -6.999800e+01
3|4 -6.208800e+00 1.483000e-01 -4.186870e+01
4|5 -4.843200e+00 1.526000e-01 -3.173190e+01
Residual Deviance: 1681.375
AIC: 1727.375 Warning: NaNs produced Value Std. Error t value p value
currentDF[, PROTEIN] 1.464393e-01 4.670921e-02 3.135127e+00 1.717795e-03
Age 1.444642e-02 5.845253e-03 2.471479e+00 1.345555e-02
Gendermale 6.044915e-01 2.869241e-03 2.106799e+02 0.000000e+00
ORdate_epoch -5.482731e-04 NaN NaN NaN
Hypertension.compositeyes -6.122961e-02 9.372731e-04 -6.532739e+01 0.000000e+00
DiabetesStatusDiabetes -1.615202e-01 9.171699e-04 -1.761071e+02 0.000000e+00
SmokerStatusEx-smoker 1.171725e-02 1.704927e-03 6.872582e+00 6.304994e-12
SmokerStatusNever smoked 3.697313e-01 8.885704e-04 4.160967e+02 0.000000e+00
Med.Statin.LLDyes 6.653033e-02 1.402579e-03 4.743430e+01 0.000000e+00
Med.all.antiplateletyes 1.757209e-01 1.506801e-03 1.166185e+02 0.000000e+00
GFR_MDRD 1.021946e-03 3.389078e-03 3.015410e-01 7.630020e-01
BMI -4.974842e-02 1.644529e-02 -3.025086e+00 2.485628e-03
MedHx_CVDNo -2.365070e-01 2.059810e-03 -1.148198e+02 0.000000e+00
stenose50-70% -4.022679e-01 3.172541e-04 -1.267967e+03 0.000000e+00
stenose70-90% -7.910671e-01 2.692888e-03 -2.937616e+02 0.000000e+00
stenose90-99% -1.156769e+00 2.578021e-03 -4.487044e+02 0.000000e+00
stenose100% (Occlusion) -1.591670e+00 7.429345e-05 -2.142410e+04 0.000000e+00
stenose50-99% -2.841604e+01 2.886289e-11 -9.845182e+11 0.000000e+00
0|1 -1.062128e+01 5.385723e-04 -1.972118e+04 0.000000e+00
1|2 -9.117742e+00 2.212875e-02 -4.120315e+02 0.000000e+00
2|3 -7.889529e+00 1.127107e-01 -6.999802e+01 0.000000e+00
3|4 -6.208768e+00 1.482915e-01 -4.186868e+01 0.000000e+00
4|5 -4.843179e+00 1.526280e-01 -3.173192e+01 5.639243e-221Session information
Version: v1.1.0
Last update: 2023-05-23
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to analyse Targets from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_
_S_
_C_
_W_
**Changes log**
* v1.1.0 Update to study database; update to bulk RNAseq data (deeper sequenced).
* v1.0.1 Fix to the start of this notebook.
* v1.0.0 Inital version.R version 4.3.0 (2023-04-21)
Platform: x86_64-apple-darwin22.4.0 (64-bit)
Running under: macOS 14.0
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /usr/local/Cellar/r/4.3.0_1/lib/R/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Amsterdam
tzcode source: internal
attached base packages:
[1] stats4 grid tools stats graphics grDevices utils datasets methods base
other attached packages:
[1] sjlabelled_1.2.0 mia_1.8.0 MultiAssayExperiment_1.26.0 TreeSummarizedExperiment_2.8.0
[5] Biostrings_2.68.1 XVector_0.40.0 SingleCellExperiment_1.22.0 MASS_7.3-60
[9] magrittr_2.0.3 annotables_0.2.0 EnhancedVolcano_1.18.0 ggrepel_0.9.3
[13] AnnotationFilter_1.24.0 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 mygene_1.36.0 org.Hs.eg.db_3.17.0
[17] DESeq2_1.40.1 SummarizedExperiment_1.30.1 MatrixGenerics_1.12.0 matrixStats_1.0.0
[21] GenomicFeatures_1.52.0 AnnotationDbi_1.62.1 Biobase_2.60.0 GenomicRanges_1.52.0
[25] GenomeInfoDb_1.36.0 IRanges_2.34.0 S4Vectors_0.38.1 BiocGenerics_0.46.0
[29] Hmisc_5.1-0 survminer_0.4.9 survival_3.5-5 GGally_2.1.2
[33] PerformanceAnalytics_2.0.4 xts_0.13.1 zoo_1.8-12 ggcorrplot_0.1.4.999
[37] corrr_0.4.4 reshape2_1.4.4 bacon_1.28.0 ellipse_0.4.5
[41] BiocParallel_1.34.2 meta_6.2-1 qqman_0.1.8 tidylog_1.0.2
[45] gridExtra_2.3 plyr_1.8.8 rmarkdown_2.22 patchwork_1.1.2.9000
[49] labelled_2.11.0 sjPlot_2.8.14 UpSetR_1.4.0 ggpubr_0.6.0
[53] forestplot_3.1.1 abind_1.4-5 checkmate_2.2.0 pheatmap_1.0.12
[57] devtools_2.4.5 usethis_2.2.0 BlandAltmanLeh_0.3.1 tableone_0.13.2
[61] openxlsx_4.2.5.2 haven_2.5.2 eeptools_1.2.5 DT_0.28
[65] knitr_1.43 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[69] purrr_1.0.1 tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
[73] data.table_1.14.8 naniar_1.0.0 tidyr_1.3.0 dplyr_1.1.2
[77] optparse_1.7.3 readr_2.1.4 pander_0.6.5 R.utils_2.12.2
[81] R.oo_1.25.0 R.methodsS3_1.8.2 worcs_0.1.10 credentials_1.3.2
loaded via a namespace (and not attached):
[1] igraph_1.4.3 ica_1.0-3 plotly_4.10.2 Formula_1.2-5 scater_1.28.0 zlibbioc_1.46.0
[7] gert_1.9.2 tidyselect_1.2.0 bit_4.0.5 lattice_0.21-8 rjson_0.2.21 blob_1.2.4
[13] urlchecker_1.0.1 S4Arrays_1.0.4 parallel_4.3.0 png_0.1-8 tinytex_0.45 cli_3.6.1
[19] bayestestR_0.13.1 ProtGenerics_1.32.0 askpass_1.1 sjstats_0.18.2 openssl_2.0.6 goftest_1.2-3
[25] textshaping_0.3.6 BiocIO_1.10.0 BiocNeighbors_1.18.0 uwot_0.1.14 curl_5.0.0 tidytree_0.4.2
[31] mime_0.12 evaluate_0.21 gsubfn_0.7 leiden_0.4.3 stringi_1.7.12 backports_1.4.1
[37] XML_3.99-0.14 httpuv_1.6.11 rappdirs_0.3.3 splines_4.3.0 getopt_1.20.3 KMsurv_0.1-5
[43] ggbeeswarm_0.7.2 sctransform_0.3.5 sessioninfo_1.2.2 DBI_1.1.3 jquerylib_0.1.4 withr_2.5.0
[49] systemfonts_1.0.4 class_7.3-22 lmtest_0.9-40 rtracklayer_1.60.0 htmlwidgets_1.6.2 fs_1.6.2
[55] biomaRt_2.56.0 labeling_0.4.2 gh_1.4.0 ranger_0.15.1 reticulate_1.29 decontam_1.20.0
[61] timechange_0.2.0 fansi_1.0.4 calibrate_1.7.7 vegan_2.6-4 irlba_2.3.5.1 commonmark_1.9.0
[67] ellipsis_0.3.2 lazyeval_0.2.2 yaml_2.3.7 scattermore_1.1 crayon_1.5.2 RcppAnnoy_0.0.20
[73] RColorBrewer_1.1-3 progressr_0.13.0 later_1.3.1 ggridges_0.5.4 codetools_0.2-19 base64enc_0.1-3
[79] profvis_0.3.8 Seurat_4.3.0 KEGGREST_1.40.0 Rtsne_0.16 estimability_1.4.1 Rsamtools_2.16.0
[85] filelock_1.0.2 rticles_0.25 sqldf_0.4-11 foreign_0.8-84 pkgconfig_2.0.3 xml2_1.3.4
[91] mathjaxr_1.6-0 GenomicAlignments_1.36.0 ape_5.7-1 spatstat.sparse_3.0-1 viridisLite_0.4.2 performance_0.10.4
[97] xtable_1.8-4 car_3.1-2 httr_1.4.6 globals_0.16.2 sys_3.4.2 SeuratObject_4.1.3
[103] pkgbuild_1.4.0 beeswarm_0.4.0 htmlTable_2.4.1 broom_1.0.4 nlme_3.1-162 dbplyr_2.3.2
[109] survMisc_0.5.6 crosstalk_1.2.0 ggeffects_1.2.2 lme4_1.1-33 digest_0.6.31 permute_0.9-7
[115] numDeriv_2016.8-1.1 Matrix_1.5-4.1 farver_2.1.1 tzdb_0.4.0 viridis_0.6.3 yulab.utils_0.0.6
[121] DirichletMultinomial_1.42.0 rpart_4.1.19 glue_1.6.2 cachem_1.0.8 BiocFileCache_2.8.0 polyclip_1.10-4
[127] generics_0.1.3 visdat_0.6.0 CompQuadForm_1.4.3 mvtnorm_1.2-1 proto_1.0.0 survey_4.2-1
[133] parallelly_1.36.0 ggtext_0.1.2 pkgload_1.3.2 arm_1.13-1 ragg_1.2.5 ScaledMatrix_1.8.1
[139] carData_3.0-5 minqa_1.2.5 pbapply_1.7-0 vroom_1.6.3 utf8_1.2.3 mitools_2.4
[145] sjmisc_2.8.9 ggsignif_0.6.4 shiny_1.7.4 GenomeInfoDbData_1.2.10 clisymbols_1.2.0 RCurl_1.98-1.12
[151] memoise_2.0.1 scales_1.2.1 future_1.32.0 reshape_0.8.9 RANN_2.6.1 renv_0.17.3
[157] km.ci_0.5-6 spatstat.data_3.0-1 rstudioapi_0.14 cluster_2.1.4 spatstat.utils_3.0-3 hms_1.1.3
[163] fitdistrplus_1.1-11 munsell_0.5.0 cowplot_1.1.1 colorspace_2.1-0 rlang_1.1.1 quadprog_1.5-8
[169] sparseMatrixStats_1.12.0 DelayedMatrixStats_1.22.0 scuttle_1.10.1 mgcv_1.8-42 xfun_0.39 prereg_0.6.0
[175] coda_0.19-4 e1071_1.7-13 TH.data_1.1-2 metafor_4.2-0 modelr_0.1.11 remotes_2.4.2
[181] emmeans_1.8.6 treeio_1.24.1 ggsci_3.0.0 DECIPHER_2.28.0 bitops_1.0-7 ps_1.7.5
[187] promises_1.2.0.1 RSQLite_2.3.1 sandwich_3.0-2 DelayedArray_0.26.3 proxy_0.4-27 compiler_4.3.0
[193] prettyunits_1.1.1 beachmat_2.16.0 boot_1.3-28.1 metadat_1.2-0 listenv_0.9.0 Rcpp_1.0.10
[199] BiocSingular_1.16.0 tensor_1.5 progress_1.2.2 gridtext_0.1.5 insight_0.19.2 spatstat.random_3.1-5
[205] R6_2.5.1 fastmap_1.1.1 multcomp_1.4-23 rstatix_0.7.2 vipor_0.4.5 ROCR_1.0-11
[211] rsvd_1.0.5 vcd_1.4-11 nnet_7.3-19 gtable_0.3.3 KernSmooth_2.23-21 miniUI_0.1.1.1
[217] deldir_1.0-9 htmltools_0.5.5 bit64_4.0.5 spatstat.explore_3.2-1 lifecycle_1.0.3 zip_2.3.0
[223] processx_3.8.1 nloptr_2.0.3 callr_3.7.3 restfulr_0.0.15 sass_0.4.6 vctrs_0.6.2
[229] spatstat.geom_3.2-1 sp_1.6-1 future.apply_1.11.0 bslib_0.4.2 pillar_1.9.0 locfit_1.5-9.7
[235] jsonlite_1.8.5 markdown_1.7 chron_2.3-61 Saving environment
| © 1979-2023 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | vanderlaan.science. | |
---
title: "Additional Figures"
author: '[Sander W. van der Laan, PhD](https://vanderlaan.science) | s.w.vanderlaan@gmail.com'
date: '`r Sys.Date()`'
output:
  html_notebook: 
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
    highlight: tango
mainfont: Helvetica
subtitle: Accompanying 'AE_TEMPLATE'
editor_options:
  chunk_output_type: inline
bibliography: references.bib
knit: worcs::cite_all
---

# General Setup

# General Setup
```{r echo = FALSE}
rm(list = ls())
```

```{r LocalSystem, echo = FALSE}
source("scripts/local.system.R")
```

```{r Source functions}
source("scripts/functions.R")
```

```{r loading_packages, message=FALSE, warning=FALSE}
source("scripts/pack02.packages.R")

```

```{r Setting: Colors}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

source("scripts/colors.R")

```

```{r setup_notebook, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()

# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/', 
                      warning = TRUE, # show warnings during codebook generation
                      message = TRUE, # show messages during codebook generation
                      error = TRUE, # do not interrupt codebook generation in case of errors, 
                                    # usually better for debugging
                      echo = TRUE,  # show R code
                      eval = TRUE)

ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")

```


# Background

This notebook contains additional figures of the project.


# Loading data

```{r Loading project data}
# load(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
load(paste0(PROJECT_loc, "/20230614.",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))


```


# Fix some variables

We need to get the 'conventional unit' versions of cholesterols.

```{r}
AERNASE.clin <- merge(AERNASE.clin.targets, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", 
                                                        "risk614", 
                                                        "LDL_finalCU", "HDL_finalCU", "TC_finalCU", "TG_finalCU")), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)
```


# Additional figures

## Age and sex
We want to create per-age-group figures median ± interquartile range. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by sex.
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by (sex and) age group (<55, 55-64, 65-74, 75-84, 85+).


```{r per Sex}

# ?ggpubr::ggboxplot()
compare_means(PCSK9 ~ Gender,  data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("Gender"),
                  y = "PCSK9", 
                  xlab = "gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Gender.pdf"), plot = last_plot())


```

```{r AgeGroups}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% dplyr::mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AERNASE.clin <- AERNASE.clin %>% dplyr::mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AERNASE.clin$AgeGroup, AERNASE.clin$Gender)
table(AERNASE.clin$AgeGroupSex)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

```{r per AgeGroup per Sex}

# ?ggpubr::ggboxplot()
compare_means(PCSK9 ~ AgeGroup,  data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("AgeGroup"),
                  y = "PCSK9", 
                  xlab = "Age groups (years)",
                  ylab = "PCSK9 (normalized expression)",
                  color = "AgeGroup",
                  palette = "npg",
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AgeGroup.pdf"), plot = last_plot())

compare_means(PCSK9 ~ AgeGroup, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("AgeGroup"),
                  y = "PCSK9", 
                  xlab = "Age groups (years) per gender",
                  ylab = "PCSK9 (normalized expression",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AgeGroup_perGender.pdf"), plot = last_plot())




```


## Hypertension & blood pressure
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by hypertension group (no, yes)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by systolic blood pressure group (<120, 120-139, 140-159,160+)


```{r BloodPressure}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
                                                     systolic >= 120  & systolic <= 139 ~ "120-139",
                                                     systolic >= 140  & systolic <= 159 ~ "140-159",
                                                     systolic >= 160 ~ "160+"))) 

table(AERNASE.clin$SBPGroup, AERNASE.clin$Gender)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and hypertension/blood pressure group as median ± interquartile range.

### PCSK9

```{r}
detach("package:EnsDb.Hsapiens.v86", unload = TRUE)
detach("package:ensembldb", unload = TRUE)

```


```{r }
compare_means(PCSK9 ~ SBPGroup, data = AERNASE.clin %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "PCSK9", 
                  xlab = "Systolic blood pressure (mmHg)",
                  ylab = "PCSK9 (normalized expression)",
                  color = "SBPGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.SBPGroup.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Hypertension.selfreport, data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "PCSK9", 
                  xlab = "Self-reported hypertension",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Hypertension.selfreport",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Hypertension.drugs, data = AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "PCSK9", 
                  xlab = "Hypertension medication use",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.HypertensionDrugs.pdf"), plot = last_plot())



compare_means(PCSK9 ~ SBPGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "PCSK9", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.SBPGroup_byGender.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Hypertension.selfreport, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "PCSK9", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension_byGender.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Hypertension.drugs, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "PCSK9", 
                  xlab = "Hypertension medication use per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())



compare_means(PCSK9 ~ SBPGroup, group.by = "Hypertension.drugs", data = AERNASE.clin %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), 
                  x = c("SBPGroup"),
                  y = "PCSK9", 
                  xlab = "Systolic blood pressure (mmHg) by medication use",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "PCSK9", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())

```


## Hypercholesterolemia & LDL levels
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by hypercholesterolemia (`risk614`) group (no, yes)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by lipid-lowering drugs group (no, yes)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)

```{r LDLGroups}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
                                                     LDL_finalCU >= 100  & LDL_finalCU <= 129 ~ "100-129",
                                                     LDL_finalCU >= 130  & LDL_finalCU <= 159 ~ "130-159",
                                                     LDL_finalCU >= 160  & LDL_finalCU <= 189 ~ "160-189",
                                                     LDL_finalCU >= 190 ~ "190+"))) 


table(AERNASE.clin$LDLGroup, AERNASE.clin$Gender)

```


```{r Fix Hypercholesterolemia, message=FALSE, warning=FALSE}
require(sjlabelled)

AERNASE.clin$risk614 <- to_factor(AERNASE.clin$risk614)

# Fix plaquephenotypes
attach(AERNASE.clin)
AERNASE.clin[,"Hypercholesterolemia"] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == "missing value"] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == -999] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == 0] <- "no"
AERNASE.clin$Hypercholesterolemia[risk614 == 1] <- "yes"
detach(AERNASE.clin)

table(AERNASE.clin$risk614, AERNASE.clin$Hypercholesterolemia)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "risk614", "Hypercholesterolemia"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users as median ± interquartile range.

### PCSK9

```{r }

compare_means(PCSK9 ~ LDLGroup, data = AERNASE.clin %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "PCSK9", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "PCSK9 (normalized expression))",
                  color = "LDLGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups.pdf"), plot = last_plot())

compare_means(PCSK9 ~ LDLGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "PCSK9", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups_byGender.pdf"), plot = last_plot())



compare_means(PCSK9 ~ Hypercholesterolemia, data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "PCSK9", 
                  xlab = "Diagnosed hypercholesterolemia",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Hypercholesterolemia",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypercholesterolemia.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Hypercholesterolemia, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "PCSK9", 
                  xlab = "Diagnosed hypercholesterolemia per gender",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())


compare_means(PCSK9 ~ Med.Statin.LLD, data = AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "PCSK9", 
                  xlab = "Lipid-lowering drug use",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Med.Statin.LLD.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Med.Statin.LLD, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "PCSK9", 
                  xlab = "Lipid-lowering drug use per gender",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())




compare_means(PCSK9 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AERNASE.clin %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "PCSK9", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), 
                  x = c("Hypercholesterolemia"),
                  y = "PCSK9", 
                  xlab = "Diagnosed hypercholesterolemia per LLD use",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())


```


## Kidney function (eGFR)
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by kidney function. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)

```{r EGFR}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
                                                             GFR_MDRD >= 15  & GFR_MDRD <= 29 ~ "15-29",
                                                             GFR_MDRD >= 30  & GFR_MDRD <= 59 ~ "30-59",
                                                             GFR_MDRD >= 60  & GFR_MDRD <= 89 ~ "60-89",
                                                             GFR_MDRD >= 90 ~ "90+")))

table(AERNASE.clin$eGFRGroup, AERNASE.clin$Gender)

table(AERNASE.clin$eGFRGroup, AERNASE.clin$KDOQI)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and kidney function group as median ± interquartile range.

### PCSK9

```{r}

# Global test

compare_means(PCSK9 ~ eGFRGroup, data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "PCSK9", 
                  xlab = "eGFR (mL/min per 1.73 m2)",
                  ylab = "PCSK9 (normalized expression)",
                  color = "eGFRGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.EGFR.pdf"), plot = last_plot())

compare_means(PCSK9 ~ eGFRGroup, group.by = "Gender",  data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "PCSK9", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.EGFR_byGender.pdf"), plot = last_plot())

compare_means(PCSK9 ~ KDOQI, data = AERNASE.clin %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "PCSK9", 
                  xlab = "Kidney function (KDOQI)",
                  ylab = "PCSK9 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.KDOQI.pdf"), plot = last_plot())

compare_means(PCSK9 ~ KDOQI, group.by = "Gender",   data = AERNASE.clin %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "PCSK9", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.KDOQI_byGender.pdf"), plot = last_plot())

compare_means(PCSK9 ~ eGFRGroup,  data = AERNASE.clin %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "PCSK9", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "PCSK9 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.EGFR_KDOQI.pdf"), plot = last_plot())

```


## BMI
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by BMI. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)

```{r BMI}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
                                                     BMI >= 18.5  & BMI < 25 ~ "18.5-24",
                                                     BMI >= 25  & BMI < 30 ~ "25-29",
                                                     BMI >= 30  & BMI < 35 ~ "30-35",
                                                     BMI >= 35 ~ "35+"))) 

# require(labelled)
# AERNASE.clin$BMI_US <- as_factor(AERNASE.clin$BMI_US)
# AERNASE.clin$BMI_WHO <- as_factor(AERNASE.clin$BMI_WHO)
# table(AERNASE.clin$BMI_WHO, AERNASE.clin$BMI_US)

table(AERNASE.clin$BMIGroup, AERNASE.clin$Gender)
table(AERNASE.clin$BMIGroup, AERNASE.clin$BMI_WHO)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

### PCSK9

```{r}

# Global test
compare_means(PCSK9 ~ BMIGroup,  data = AERNASE.clin %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "PCSK9", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "PCSK9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "BMIGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.BMI.pdf"), plot = last_plot())

compare_means(PCSK9 ~ BMIGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "PCSK9", 
                  xlab = "BMI groups (kg/m2) per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.BMI_byGender.pdf"), plot = last_plot())

compare_means(PCSK9 ~ BMIGroup,  data = AERNASE.clin %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("BMIGroup"),
                  y = "PCSK9", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "PCSK9 (normalized expression)",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.BMI_byWHO.pdf"), plot = last_plot())

```


## Diabetes
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by type 2 diabetes. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by type 2 diabetes group (no, yes)

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

### PCSK9

```{r per Diabetes per Sex}

compare_means(PCSK9 ~ DiabetesStatus,  
              data = AERNASE.clin %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(DiabetesStatus)),
                  x = c("DiabetesStatus"),
                  y = "PCSK9",
                  xlab = "Diabetes status",
                  ylab = "PCSK9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "DiabetesStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Diabetes.pdf"), plot = last_plot())


compare_means(PCSK9 ~ DiabetesStatus, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(DiabetesStatus)),
                  x = c("DiabetesStatus"),
                  y = "PCSK9",
                  xlab = "Diabetes status per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Diabetes_byGender.pdf"), plot = last_plot())
  

```


## Smoking
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by smoking. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by smoking group (never, ex, current)

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

### PCSK9 

```{r per Smoking per Sex}

# Global test
compare_means(PCSK9 ~ SmokerStatus,  data = AERNASE.clin %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "PCSK9", 
                  xlab = "Smoker status",
                  ylab = "PCSK9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "SmokerStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Smoking.pdf"), plot = last_plot())

compare_means(PCSK9 ~ SmokerStatus, group.by ="Gender", data = AERNASE.clin %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "PCSK9", 
                  xlab = "Smoker status per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Smoking_byGender.pdf"), plot = last_plot())

```


## Stenosis
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by stenosis grade. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by stenosis grade group (<70, 70-89, 90+)

```{r Stenosis}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
                                                     stenose == "0-49%" ~ "<70",
                                                     stenose == "50-70%" ~ "<70",
                                                     stenose == "70-90%" ~ "70-89",
                                                     stenose == "50-99%" ~ "90+",
                                                     stenose == "70-99%" ~ "90+",
                                                     stenose == "100% (Occlusion)" ~ "90+",
                                                     stenose == "90-99%" ~ "90+")))

table(AERNASE.clin$StenoticGroup, AERNASE.clin$Gender)
table(AERNASE.clin$stenose, AERNASE.clin$StenoticGroup)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

### PCSK9

```{r per Stenosis per Sex}

# Global test
compare_means(PCSK9 ~ StenoticGroup,  data = AERNASE.clin %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "PCSK9", 
                  xlab = "Stenotic grade",
                  ylab = "PCSK9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "StenoticGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Stenosis.pdf"), plot = last_plot())

compare_means(PCSK9 ~ StenoticGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "PCSK9", 
                  xlab = "Stenotic grade per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Stenosis_byGender.pdf"), plot = last_plot())

```


## Symptoms
We want to create per-symptom figures. 

```{r SymptomGroups}
library(dplyr)

table(AERNASE.clin$AgeGroup, AERNASE.clin$AsymptSympt2G)
table(AERNASE.clin$Gender, AERNASE.clin$AsymptSympt2G)
table(AERNASE.clin$AsymptSympt2G)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per symptom group as median ± interquartile range.

### PCSK9

```{r per SymptomGroups}

# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "AsymptSympt2G", y = "PCSK9",
                  title = "PCSK9 (normalized expression) levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "PCSK9 (normalized expression)",
                  color = "AsymptSympt2G", 
                  # palette = c(uithof_color[16], uithof_color[23]),
                  palette = "npg",
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AsymptSympt2G.pdf"), plot = last_plot())

rm(p1)

compare_means(PCSK9 ~ AsymptSympt2G, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "AsymptSympt2G", y = "PCSK9",
                  title = "PCSK9 (normalized expression) levels per symptom by gender", 
                  xlab = "Symptoms",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AsymptSympt2G.byGender.pdf"), plot = last_plot())

rm(p1)

```


## Forest plots

We would also like to visualize the multivariable analyses results.
```{r load model data}
library(ggplot2)
library(openxlsx)
model1_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.targets.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"))
model2_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.targets.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"))
model1_target$model <- "univariate"
model2_target$model <- "multivariate"

models_target <- rbind(model1_target, model2_target)
models_target

```

Forest plots.

### PCSK9

```{r forestplots}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_target$OR[models_target$Predictor=="PCSK9"]),
                  low = c(models_target$low95CI[models_target$Predictor=="PCSK9"]),
                  high = c(models_target$up95CI[models_target$Predictor=="PCSK9"]))

fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
  geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) + 
  geom_hline(yintercept = 1, lty = 2) +  # add a dotted line at x=1 after flip
  coord_flip(ylim = c(0.8, 1.7)) +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  ggtitle("Plaque PCSK9 normalized expression (1 SD increment, n = 622)") +
  theme_minimal()  # use a white background
print(fp)

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.forest.pdf"), plot = fp)

rm(fp)
```


## Target expression vs. cytokines plaque levels correlations

We will plot the correlations of other cytokine plaque levels to the `r TRAIT_OF_INTEREST` plaque levels. These include:

- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay. 

- MMP2
- MMP8
- MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the `r TRAIT_OF_INTEREST`` plaque levels.


We will set the measurements that yielded '0' to NA, as it is unlikely that any protein ever has exactly 0 copies. The '0' yielded during the experiment are due to the limits of the detection.

### Prepare data

```{r}
# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"

# fix names
names(AERNASE.clin)[names(AERNASE.clin) == "IL6"] <- "IL6rna"

cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")


AERNASE.clin <- merge(AERNASE.clin, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", 
                                                        cytokines,
                                                        metalloproteinases)), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)

```


```{r Target vs Cytokines INRT, paged.print=TRUE}

proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))

# make variables numerics()
AERNASE.clin <- AERNASE.clin %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
  
for(PROTEIN in 1:length(proteins_of_interest)){

  var.temp.rank = proteins_of_interest_rank[PROTEIN]
  var.temp = proteins_of_interest[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  # AERNASE.clin <-  AERNASE.clin %>% mutate(AERNASE.clin[,var.temp] == replace(AERNASE.clin[,var.temp], AERNASE.clin[,var.temp]==0, NA))

  AERNASE.clin[,var.temp][AERNASE.clin[,var.temp]==0.000000]=NA

  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AERNASE.clin[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AERNASE.clin[,var.temp])),").\n"))
  
  AERNASE.clin <- AERNASE.clin %>%
      mutate_at(vars(var.temp), 
        # list(Z = ~ (AERNASE.clin[,var.temp] - mean(AERNASE.clin[,var.temp], na.rm = TRUE))/sd(AERNASE.clin[,var.temp], na.rm = TRUE))
        list(RANK = ~ qnorm((rank(AERNASE.clin[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin[,var.temp]))))
      )
  # str(UCORBIOGSAqc$Z)
  cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
  AERNASE.clin[,var.temp.rank] <- NULL
  names(AERNASE.clin)[names(AERNASE.clin) == "RANK"] <- var.temp.rank
}

# rm(var.temp, var.temp.rank)

```

### Visualize transformations

We will just visualize these transformations.

```{r Target vs Cytokines Histograms}
proteins_of_interest_rank_target <- c("PCSK9", proteins_of_interest_rank)

proteins_of_interest_target <- c("PCSK9", proteins_of_interest)

for(PROTEIN_GENE in proteins_of_interest_target){
  cat(paste0("Plotting protein ", PROTEIN_GENE, ".\n"))
  
  p1 <- ggpubr::gghistogram(AERNASE.clin, PROTEIN_GENE,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN_GENE, " (normalized expression)"),
                    xlab = "",
                    ggtheme = theme_minimal())
  print(p1)
  
}


for(PROTEIN_GENE in proteins_of_interest_rank_target){
  cat(paste0("Plotting protein ", PROTEIN_GENE, ".\n"))
  
  p1 <- ggpubr::gghistogram(AERNASE.clin, PROTEIN_GENE,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN_GENE, " (normalized expression)"),
                    xlab = "inverse-normal transformation",
                    ggtheme = theme_minimal())
  print(p1)
  
}
  
```

### Correlations

Here we calculate correlations between `r TRAIT_OF_INTEREST` and 28 other cytokines. We use Spearman's test, thus, correlations a given in _rho_. Please note the indications of measurement methods:

- _L_: LUMINEX
- _E_: ELISA
- _a_: activity assay

```{r Target vs Cytokines correlations}
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages.auto("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - inverse-rank transformation
# --------------------------------
temp <- subset(AERNASE.clin, 
                          select = c(proteins_of_interest_rank_target)
                                    )

# str(AEDB.CEA.temp)
matrix.RANK <- as.matrix(temp)
rm(temp)

corr_biomarkers.rank <- round(cor(matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

rename_proteins_of_interest_target <- c("PCSK9 (RNA)", 
                                    "IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", 
                                    "IL13 (L)", "IL21 (L)", 
                                    "INFG", "TNFA", "MIF (L)", 
                                    "MCP1 (L)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)", 
                                    "Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)", 
                                    "OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}

corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)


names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"

DT::datatable(corr_biomarkers.rank.df)

fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))

```

```{r Target vs Cytokines heatmap}
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           tl.cex = 16,
           # xlab = c("MCP1"),
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.pdf"), plot = last_plot())

rm(p1)

```

While visually attractive we are not necessarily interested in the correlations between all the cytokines, rather of `r TRAIT_OF_INTEREST`` with other cytokines only.

### PCSK9 

```{r Target vs Cytokines barplot}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "PCSK9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold

p1 <- ggpubr::ggbarplot(temp, 
                x = "CytokineY", 
                y = "SpearmanRho",
                fill = "CytokineY",               # change fill color by cyl
                # color = "white",            # Set bar border colors to white
                # palette = uithof_color,            # jco journal color palett. see ?ggpar
                xlab = "Cytokine",
                sort.val = "desc",          # Sort the value in dscending order
                sort.by.groups = FALSE,     # Don't sort inside each group
                x.text.angle = 45, # Rotate vertically x axis texts
                cex = 1.25
                )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 14),
        axis.text.y = element_text(size = 14),
        axis.title.x = element_text(size = 18),
        axis.title.y = element_text(size = 18)) +
  labs(y = expression(paste("Spearman's"~italic(rho))))

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.pdf"), plot = last_plot())
rm(p1)


```

Another version - probably not good. 
```{r}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "PCSK9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold
p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
           color = "CytokineY", #fill = "CytokineY",                              # Color by groups
           # palette = uithof_color, # Custom color palette
           xlab = "Cytokine",
           # ylab = expression(log[10]~"("~italic(p)~")-value"),
           # ylim = c(0, 9),
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = FALSE,                                # Rotate vertically
           # group = "CytokineY",                                # Order by groups
           dot.size = 8,                                 # Large dot size
           label = round(temp$SpearmanRho, digits = 3),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 4, 
                             vjust = 0.5)                   
           )
ggpar(p1, legend = "", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 14),
        axis.text.y = element_text(size = 14),
        axis.title.x = element_text(size = 18),
        axis.title.y = element_text(size = 18)) +
  labs(y = expression(log[10]~"("~italic(p)~")-value"))

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.dotchart.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.dotchart.pdf"), plot = last_plot())

rm(temp, p1)

```


## Target expression vs. cytokines plaque levels `lm()`

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines traits as a function of plaque `r TRAIT_OF_INTEREST` levels.

```{r CrossSec: Cytokines - linear regression MODEL1 RANK, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.TARGET.RANK)) {
  PROTEIN = TRAITS.TARGET.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

```

```{r CrossSec: Cytokines - linear regression MODEL1 RANK Writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.Con.Uni.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
           rowNmes = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```



### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines as a function of plaque `r TRAIT_OF_INTEREST` levels.

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.TARGET.RANK)) {
  PROTEIN = TRAITS.TARGET.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data = currentDF)
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

```

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.Con.Multi.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

## Target expression vs. vulnerability index


Here we plot the levels of inverse-rank normal transformed `r TRAIT_OF_INTEREST` plaque levels from experiment 1 and 2 to the `Plaque vulnerability index`. 

```{r Fix ORyearGroup, message=FALSE, warning=FALSE}
library(sjlabelled)

AERNASE.clin$yeartemp <- as.numeric(year(AERNASE.clin$dateok))

attach(AERNASE.clin)

AERNASE.clin[,"ORyearGroup"] <- NA
AERNASE.clin$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AERNASE.clin$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AERNASE.clin)

table(AERNASE.clin$ORyearGroup, AERNASE.clin$ORdate_year)
```

### Visualisations

#### PCSK9

```{r per PlaqueVulnerabilityIndex}
# Global test

compare_means(PCSK9 ~ Plaque_Vulnerability_Index,  data = AERNASE.clin, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "PCSK9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "PCSK9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "PCSK9", 
                  xlab = "Plaque vulnerability index by gender",
                  ylab = "PCSK9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())




compare_means(PCSK9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin, method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "PCSK9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "PCSK9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "PCSK9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "PCSK9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex_Facet_byYear.byGender.pdf"), plot = last_plot())

```


### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of the plaque vulnerability index as a function of plaque `r TRAIT_OF_INTEREST` levels.

```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL1 RANK, paged.print=TRUE}
TRAITS.TARGET.RANK.extra = c("PCSK9")

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
  PROTEIN = TRAITS.TARGET.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    # currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    # table(currentDF$ORdate_year)
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    # fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year, 
    #           data  =  currentDF, 
    #           Hess = TRUE)
    fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch, 
              data  =  currentDF, 
              Hess = TRUE)
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }


```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL2 RANK, paged.print=TRUE}

for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
  PROTEIN = TRAITS.TARGET.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    # currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    # fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
    #           data  =  currentDF,
    #           Hess = TRUE)
    
    fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
              data  =  currentDF,
              Hess = TRUE)
    
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }

```


# Session information

--------------------------------------------------------------------------------

    Version:      v1.1.0
    Last update:  2023-05-23
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse Targets from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_

    _C_

    _W_

    **Changes log**
    * v1.1.0 Update to study database; update to bulk RNAseq data (deeper sequenced).
    * v1.0.1 Fix to the start of this notebook.
    * v1.0.0 Inital version.
    

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.additional_figures.RData"))
```

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| <sup>© 1979-2023 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | [vanderlaan.science](https://vanderlaan.science).</sup> |
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